Why and When Does a Mindfulness Intervention Promote Job Performance? The

Interpersonal Mechanisms and Individual, Job, and Social Contingencies

A DISSERTATIONSUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL

OF THE UNIVERSITY OF MINNESOTABY

Tao Yang

IN PARTIAL FULFILLMENT OF THE REQUIREMENTSFOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Theresa M. Glomb, Adviser

July, 2015

© Tao Yang 2015

i

Acknowledgements

I am deeply grateful to my advisor and friend, Dr. Theresa Glomb, for her

guidance and support throughout my graduate career. It is an honor to have worked with

her over the past six years and I have learned numerous things from her. Her optimism,

talent, and integrity have been influential to me, personally and professionally. She will

undoubtedly be a constant source of inspiration for my development.

I would like to thank my dissertation committee members, Drs. Michelle Duffy,

Joyce Bono, Patricia Frazier, and Elizabeth Campbell, for their insightful comments. I

thank my colleagues at the Department of Work and Organizations, especially Drs. John

Kammeyer-Mueller, Pri Shah, Mary Zellmer-Bruhn, Sophie Leroy, and Betty Zhou, for

enriching many aspects of my professional life. I thank my fellow doctoral students at the

Department for creating an intellectually stimulating, caring, and fun social environment.

I gratefully acknowledge the dissertation grant awards from the SIOP Foundation

Benjamin Schneider Graduate Scholarship and the Department of Work and

Organizations Dissertation Research Grant. I would like to extend special appreciation to

my organizational participants who were willing to take the time to participate in study

activities amidst their already full lives. I also thank Drs. Zhen Zhang and Kristopher

Preacher for helpful suggestions on data analysis.

Finally, I am indebted to my parents for their unconditional love. I am especially

grateful to my fiancée, Shan Zhao. She is a constant source of joy and strength in my life.

ii

Abstract

This dissertation develops and tests a theoretical model of the role of a mindfulness

intervention in promoting job performance in service settings. I examine the client-

focused mechanisms—attentiveness, perspective taking, and response flexibility—and

individual (i.e., employee agreeableness), social (i.e., perception of workgroup service

climate), and job (i.e., work overload) contingencies of the relationship between a

mindfulness intervention and job performance. I conducted a pretest-posttest field

experiment of 72 health care professionals in a health care organization with intervention

(i.e., mindfulness meditation) and active control (i.e., wellness education) conditions and

repeated measures from health care professionals and their patients over 15 days.

Confirmatory factor analyses suggest that the three client-focused mechanisms were

represented by a higher-order construct of patient centered behavior. Multilevel modeling

and latent growth modeling suggest that the two conditions are distinct; compared with

active control, the intervention yields pre-to-post increases in daily mindfulness and work

behaviors including self-ratings of job performance and proactive patient care and patient

ratings of patient centered behavior. Multilevel mediation analysis suggests that patient

ratings of patient centered behavior fail to mediate the effect of a mindfulness

intervention on patient satisfaction with job performance. Multilevel moderated

mediation analyses suggest that agreeableness, perceived workgroup service climate, and

work overload do not moderate the effect of a mindfulness intervention (via patient

ratings of patient centered behavior) on patient satisfaction. Nonetheless, compared with

active control, the mindfulness intervention yields higher patient rated patient centered

behavior for health care professionals who have a higher level of agreeableness.

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Table of Contents

List of Tables .......................................................................................................................vList of Figures .................................................................................................................. viiiChapter 1: Introduction ........................................................................................................1Chapter 2: Mindfulness and Job Performance .....................................................................6

Conceptualization of Mindfulness ...................................................................................6Conceptualization and Prediction of Job Performance ...................................................6Mindfulness and Job Performance ..................................................................................8

Chapter 3: Mechanisms......................................................................................................12Mediating Effect of Attentiveness..................................................................................12Mediating Effect of Perspective Taking.........................................................................14Mediating Effect of Response Flexibility ......................................................................17

Chapter 4: Boundary Conditions .......................................................................................20Moderating Effect of Agreeableness .............................................................................20Moderating Effect of Perceived Workgroup Service Climate ......................................22Moderating Effect of Perceived Work Overload ...........................................................24

Chapter 5: Method .............................................................................................................26Overview .......................................................................................................................26Sample ...........................................................................................................................26Procedure........................................................................................................................27Conditions ......................................................................................................................31Measures: Background Survey.......................................................................................34Measures: End-of-Workday Survey...............................................................................40Measures: Patient Survey ...............................................................................................44

Chapter 6: Results ..............................................................................................................47Overview .......................................................................................................................47Confirmatory Factor Analysis of Patient Interaction Items ..........................................48Description of Study Variables .....................................................................................53Effect of Intervention on Participant and Patient Outcomes .........................................54Cross-Classified Multilevel Moderated Mediation Analyses: Analytic Strategies ......85Nested Data Structure ...................................................................................................86Mediating Effect of Patient-Rated Patient Centered Behavior .....................................87Moderating Effect of Agreeableness .............................................................................88Moderating Effect of Perceived Workgroup Patient Care Climate ...............................89Moderating Effect of Perceived Daily Work Overload .................................................90Supplemental Analyses .................................................................................................92

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Chapter 7: Discussion ........................................................................................................93Summary of Study Findings ..........................................................................................93Theoretical and Empirical Implications ........................................................................93Practical Implications ..................................................................................................100Limitations and Future Directions ...............................................................................101Conclusion....................................................................................................................103

Tables and Figures ...........................................................................................................104References........................................................................................................................160Appendix I: Recruitment Materials ................................................................................187Appendix II: Consent Forms............................................................................................188Appendix III: Surveys......................................................................................................196Appendix IV: Active Phase Materials ............................................................................216Appendix V: Debriefing Materials .................................................................................219Appendix VI: Supplemental Results of Latent Growth Modeling using Five Waves of Repeated Measures ..........................................................................................................223

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List of Tables

Table 1: Summary of Studies Linking Mindfulness to Job Performance.......................104Table 2: Standardized Item Loadings in the Higher-Order-Factor Model in the Participant Background Survey .......................................................................................108Table 3: Standardized Item Loadings from the Multilevel Confirmatory Factor Analysis of the Higher-Order-Factor Model of Patient Interaction Items in the Participant End-of-Workday Survey .............................................................................................................109Table 4: Standardized Item Loadings from the Cross-Classified Confirmatory Factor Analysis of the Higher-Order-Factor Model of Patient Interaction Items in the Patient Survey .............................................................................................................................110Table 5: -Order Correlations of Study Variables in the Participant Background Survey ............................................................111Table 6: -Order Correlations of Participant Reported Daily Study Variables in the Participant End-of-Workday Survey..........................................................................................................................................116Table 7: -Order Correlations of Study Variables in the Patient Survey........................................................................................118Table 8: Pre- and Post-Intervention Means and Standard Deviations for Study Variables by Condition.....................................................................................................................119Table 9: Summary of Results from Multilevel Modeling (MLM) and Latent Growth Modeling (LGM) of Daily Outcomes during Intervention for Participants Attending At Least One Audio ..............................................................................................................121Table 10: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Mindfulness over Intervention Period ...............................................................................................................................122Table 11: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Job Satisfaction over Intervention Period ...............................................................................................................................123Table 12: Multilevel Modeling Results for the Effect of Experimental Condition andTotal Number of Audios Attended (TNAA) on Daily Job Performance over Intervention Period ...............................................................................................................................124Table 13: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Proactive Patient Care over Intervention Period...........................................................................................................125Table 14: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Participant-Rated Patient Centered Behavior over Intervention Period...................................................................................126Table 15: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Stress over Intervention Period ..127

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List of Tables (Continued)

Table 16: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Emotional Exhaustion over Intervention Period...........................................................................................................128Table 17: Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Fatigue over Intervention Period129Table 18: Cross-Classified Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Encounter-Level Patient Rated Patient Centered Behavior over Intervention Period.............................................130Table 19: Cross-Classified Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Encounter-Level Patient Satisfaction over Intervention Period ..............................................................................131Table 20: Key Parameter Estimates for Final Models of Normative Growth Trajectory of Daily Outcomes ...............................................................................................................132Table 21: Fit Statistics for Linear Growth Model of Daily Outcome by Condition Using 10 Days of Repeated Measures during Intervention........................................................133Table 22: Parameter Estimates of Linear Growth Model of Daily Outcome by Condition Using 10 Days of Repeated Measures during Intervention .............................................134Table 23: Fit Statistics for Linear Growth Model of Daily Outcome by Condition Using 5 Waves of Repeated Measures during Intervention ..........................................................135Table 24: Parameter Estimates of Linear Growth Model of Daily Outcome by Condition Using 5 Waves of Repeated Measures during Intervention.............................................136Table 25: Two-Group Linear Growth Models for Daily Mindfulness over Intervention Period Using 10-Day and 5-Wave Repeated Measures ...................................................137Table 26: Two-Group Linear Growth Models for Daily Job Satisfaction over Intervention Period Using 10-Day and 5-Wave Repeated Measures ...................................................138Table 27: Two-Group Linear Growth Models for Daily Job Performance over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..............................139Table 28: Two-Group Linear Growth Models for Daily Proactive Patient Care over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..............................140Table 29: Two-Group Linear Growth Models for Daily Participant Rated Patient Centered Behavior over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..........................................................................................................................141Table 30: Two-Group Linear Growth Models for Daily Stress over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..............................................................142Table 31: Two-Group Linear Growth Models for Daily Emotional Exhaustion overIntervention Period Using 10-Day and 5-Wave Repeated Measures ..............................143Table 32: Two-Group Linear Growth Models for Daily Fatigue over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..............................................................144

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List of Tables (Continued)

Table 33: Two-Group Linear Growth Models for Daily Patient Rated Patient Centered Behavior over Intervention Period Using 10-Day and 5-Wave Repeated Measures.......145Table 34: Two-Group Linear Growth Models for Daily Patient Satisfaction over Intervention Period Using 10-Day and 5-Wave Repeated Measures ..............................146Table 35: Mean Growth Rate Estimates of Treatment and Active Control Intervention in the Final Two-Group Linear Growth Model of Daily Outcome Using 10-Day and 5-Wave Repeated Measures during Intervention for Participants Attending At Least Five Audios..........................................................................................................................................147Table 36: Cross-Classified Multilevel Mediation Model of Effect of Experimental Condition on Encounter-Specific Patient Satisfaction (PS) via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior (PCB) ........................................148Table 37: Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Agreeableness on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS) ....................................................................................149Table 38: Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Perceived Workgroup Patient Care Climate (PWPCC) on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)..................150Table 39: Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Daily Work Overload on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)...................................................................151Table 40: Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Agreeableness, Perceived Workgroup Patient Care Climate (PWPCC), and Daily Work Overload on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS) ..................................................................................................152Table 41: Participant-Level Path-Analytic Results of Indirect and Total Effects of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS) at Low and High Levels of Participants’ Agreeableness, Perceived Workgroup Patient Care Climate, and Daily Work Overload, respectively...........................................................................153

viii

List of Figures

Figure 1: Theoretical Model ...........................................................................................154

Figure 2: Daily Data Collection Scheme over 15-Day Period in the Active Phase ........155

Figure 3: Growth Modeling of Repeated Measures of Outcome over 10-Day Intervention Period ..............................................................................................................................156

Figure 4: Moderated Effect of Experimental Condition on Participant Self-Reported Daily Proactive Patient Care at Low and High Levels of Total Number of Audios Attended ..........................................................................................................................157

Figure 5: Moderated Effect of Experimental Condition on Patient Rated Patient Centered Behavior at Low and High Levels of Total Number of Audios Attended ......................158

Figure 6: Moderated Effect of Experimental Condition on Patient Rated Patient Centered Behavior at Low and High Levels of Agreeableness.......................................................159

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Chapter 1: Introduction

Mindfulness, or the human capacity of nonjudgmental awareness of and attention

to present-moment internal and external experiences (Brown & Ryan, 2003; for a review,

see Hart, Ivtzan, & Hart, 2013), has received surging attention in recent years. A recent

Google search of mindfulness yields about 29 million links and Amazon offers more than

9,200 books about mindfulness. Mindfulness meditation (Kabat-Zinn, 1990), a set of

secular practices aiming to cultivate mindfulness and well-being, has entertained

increasing popularity throughout the United States; numerous meditation centers have

blossomed across communities and a multitude of “apps” for mindfulness exercises have

been created. Organizations, such as Google, Apple, and General Mills, have begun to

incorporate customized mindfulness-based practices into employees’ work lives to

diffuse stress and promote well-being.

In addition to attention from the populace, researchers from a variety of

disciplines, ranging from psychiatry to neuroscience to applied psychology, have devoted

considerable scholarly attention to mindfulness. Research abounds attesting to the

beneficial roles mindfulness and mindfulness-based practices play for healthy and clinical

populations in cognitive and physiological functioning, emotion regulation, stress

reduction, interpersonal relationships, and mental and physical health (for reviews, see

Brown, Ryan, & Creswell, 2007; Chiesa & Serretti, 2010; Glomb, Duffy, Bono, & Yang,

2011; H lzel et al., 2011; Keng, Smoski, & Robins, 2011; for meta-analyses, see

Bohlmeijer, Prenger, Taal, & Cuijpers, 2010; Chiesa & Serretti, 2009; Grossman,

Niemann, Schmidt, & Walach, 2004; Hofmann, Sawyer, Witt, & Oh, 2010; Sedlmeier et

al., 2012).

2

Recent developments in organizational research also suggest that mindfulness and

mindfulness-based practices have implications for work-related attitudinal and well-being

outcomes, including job satisfaction (Hülsheger, Alberts, Feinholdt, & Lang, 2013; Reb,

Narayanan, & Ho, 2015), stress and burnout (Aikens et al., 2014; Bazarko, Cate, Azocar,

& Kreitzer, 2013; Beddoe & Murphy, 2004; Fortney, Luchterhand, Zakletskaia, Zgierska,

& Rakel, 2013; Krasner et al., 2009; Roeser et al., 2013; Wolever et al., 2012), anxiety

and depression (Roche, Haar, & Luthans, 2014), work-family conflict and balance (Allen

& Kiburz, 2012; Kiburz, 2012), resilience (Aikens et al., 2014), and sleep quality

(Hülsheger et al., 2014; Wolever et al., 2012). Qualitative research has also identified

changes in worker outcomes, such as reduced stress and burnout, as a result of

mindfulness training (Beckman et al., 2012; Irving et al., 2014). Recent research focusing

on mindfulness and work-related behaviors has suggested that higher dispositional

mindfulness was associated with higher communication quality (Beach et al., 2013) and a

mindfulness training facilitated employee engagement (Leroy, Anseel, Dimitrova, &

Sels, 2013). A recent study has also suggested that mindfulness meditation attenuates

retaliatory responses to injustice (Long & Christian, 2015).

A small but growing body of research has focused attention on the role

mindfulness and mindfulness-based practices play in affecting job performance, an

important criteria in the workplace. Yet the limited evidence so far has revealed an

equivocal link between mindfulness and job performance. Some studies have

demonstrated positive relationships between dispositional mindfulness and task

performance (Dane & Brummel, 2014; Reb et al., 2015), whereas others have yielded

nonsignificant relationships (Shao & Skarlicki, 2009; Zhang, Ding, Li, & Wu, 2013).

3

Evidence from a field intervention (Giluk, 2010) has suggested that mindfulness-based

practices fall short in producing within-person performance increments over time. These

inconsistent findings point to the possibility that the role of mindfulness and mindfulness-

based practices in job performance may be conditional or indirect. Indeed, recent

theorizing has highlighted task and contextual features as contingencies for the

performance effect of mindfulness (Dane, 2011; Glomb et al., 2011), and extant empirical

research, where mediating mechanisms are often unexamined or unsupported, remains

silent with regard to the theoretical pathways by which mindfulness links to performance

(Dane & Brummel, 2014; Giluk, 2010; Reb et al., 2015; Shao & Skarlicki, 2009; Zhang

et al., 2013). Past research has relied heavily on research designs using correlational data,

single-group intervention with pre- and post-tests, or random assignment without active

control groups, which have drawbacks in making strong causal inferences (Goyal et al.,

2014; MacCoon et al., 2012; Shadish, Cook, & Campbell, 2002). Indeed, in a recent

meta-analysis of meditation research, Sedlmeier et al. (2012) removed almost 75% of the

initially identified studies due to methodological problems. A recent review of stress

management and well-being promotion interventions in the workplace advocates that

scholars should “strive to identify comparison groups” (Tetrick & Winslow, 2015, p.

599). Overall, organizational research is at the dawn of fully understanding why and when

mindfulness and mindfulness-based practices may influence job performance. As Glomb

and her colleagues noted, “The time is ripe to carefully examine the role that mindfulness

might play in the performance and well-being of individuals at work” (Glomb et al.,

2011, p.116).

4

This dissertation aims to address the “why and when” questions by examining the

theoretical pathways of, and contingencies for, the association between a micro

mindfulness intervention and job performance in service settings. Drawing on Glomb et

al.’s (2011) self-regulation framework of mindfulness, I propose that a mindfulness

intervention, compared with an active control intervention, promotes other-focused work

behaviors, which, in turn, contribute to job performance. I focus on three other-focused

work behaviors: (1) attentiveness, (2) perspective taking, and (3) response flexibility. In

response to the recent call for better understanding of dispositional and organizational

factors influencing how individuals respond to workplace interventions (Tetrick &

Winslow, 2015), I examined the boundary condition in which the proposed mediation

chain may be strengthened with a focus on the factors influencing the effect of a

mindfulness intervention on client-focused work behaviors. I focused on three boundary

conditions: workers’ dispositional agreeableness as an individual contingency, perceived

workgroup service climate as a social contingency, and perceived work overload as a job

contingency. Figure 1 depicts the theoretical model.

Using health care professionals in a health care organization in the United States,

I conducted a pretest-posttest field experiment with intervention (i.e., mindfulness

meditation) and active control (i.e., wellness education) groups and with repeated

measures from participants and patients over 15 days. I focused on a health care setting

because the health care industry is one of the biggest contributors of U.S. economy and

health care workers amount to more than 14 million in 2010, approximately 10% of the

U.S. workforce (U.S. Department of Health and Human Services, 2013). The rigorous

design that randomly assigns participants into conditions and uses an active control

5

condition allows for making stronger causal inferences of the effect of mindfulness

intervention. Because the active control group closely parallels design elements of the

treatment group except contents of treatment, I examined whether treatment and active

control have differential impact on an array of workers’ attitudinal, work, and well-being

outcomes.

This dissertation contributes to management research in three important ways.

First, I extend the management literature by explicating client-focused work behaviors in

terms of attentiveness, perspective taking, and response flexibility as underlying

mechanisms of the role of a mindfulness intervention on job performance, offering a

fuller account of the “active ingredients” of mindfulness at work (Glomb et al., 2011, p.

143). Second, I explicate individual, social, and job contingencies that may accentuate the

effect of a mindfulness intervention on client-focused behaviors, thereby identifying the

boundary conditions in which a mindfulness intervention may be more effective. Lastly, I

use a rigorous design to make strong causal inferences of the effect of mindfulness

intervention and provide empirical evidence of the effectiveness of a micro mindfulness

intervention compared with an active control intervention.

Practically, this dissertation provides guidance for management by examining a

customized micro mindfulness exercise that may be incorporated into organizations’

employee wellness initiatives and has the potential to promote employees’ job

performance in the workplace. Such micro interventions, if efficacious, are more likely to

be adopted by organizations and their employees to promote performance and well-being.

6

Chapter 2: Mindfulness and Job Performance

Conceptualization of Mindfulness

Mindfulness has been defined in many ways (e.g., Baer, Smith, Hopkins,

Krietemeyer, & Toney, 2006; Bishop et al., 2004; Brown et al., 2007) with a shared tenet

that mindfulness involves receptive awareness of and attention to present moment

experiences without judgment (Brown et al., 2007). In this dissertation, drawing on

Glomb et al. (2011, p. 119), mindfulness is defined as “a state of consciousness

characterized by receptive attention to and awareness of present events and experiences,

without evaluation, judgment, and cognitive filters” (italics in original; see also Brown &

Ryan, 2003). Mindfulness is inherently a state-like construct; an individual can be more

mindful at a particular moment and less so at another. Mindfulness can be a trait-like

construct that depicts an individual’s average level of state mindfulness over time (e.g.,

Glomb et al., 2011). Mindfulness-based practices, such as mindfulness meditation and

body scan of the Mindfulness-Based Stress Reduction program (Kabat-Zinn, 1990), have

been construed as techniques designed to cultivate mindfulness by fostering present

moment awareness of and attention to internal and external stimuli in an observer stance

without judgment. In this dissertation, I adopt the activity of sitting meditation from the

MBSR as a mindfulness intervention.

Conceptualization and Prediction of Job Performance

Job performance has been commonly conceptualized as task performance,

organizational citizenship behavior, and counterproductive work behaviors (Rotundo &

Sackett, 2002; Motowidlo, 2003). In this dissertation, I focus on task performance,

defined as in-role behaviors that contribute to “the technical core” of a job (Rotundo &

7

Sackett, 2002, p. 67). I focus on task performance because it contributes to organizational

effectiveness and is of growing interest to scholars in mindfulness research (e.g., Dane,

2011; Glomb et al., 2011).

Exploring antecedents of job performance has been a venerable tradition in

organizational research. Decades of investigation have garnered extensive evidence

documenting predictors and correlates of job performance. Research has established a

broad spectrum of correlates and predictors of job performance, including individual

demographic attributes such as age and race (for meta-analyses, see McKay & McDaniel,

2006; Ng & Feldman, 2008; Roth, Huffcutt, & Bobko, 2003), general mental ability (for

a meta-analysis, see Salgado et al., 2003), dispositional attributes such as Big Five

personality traits, positive and negative affectivity, and core self-evaluations (for meta-

analyses, see Barrick & Mount, 1991; Chiaburu, Oh, Berry, Li, & Gardner, 2011;

Dudley, Orvis, Lebiecki, & Cortina, 2006; Hurtz & Donovan, 2000; Ilies, Fulmer,

Spitzmuller, & Johnson, 2009; Judge & Bono, 2001; Judge, Rodell, Klinger, Simon, &

Crawford, 2013; Kaplan, Bradley, Luchman, & Haynes, 2009), job attitudes such as

commitment, satisfaction, and justice perceptions (for meta-analyses, see Colquitt et al.,

2001; Colquitt et al., 2013; Cooper-Hakim & Viswesvaran, 2005; Hoffman, Blair,

Meriac, & Woehr, 2007; Judge, Thoresen, Bono, & Patton, 2001; Lee, Carswell, & Allen,

2000; LePine, Erez, & Johnson, 2002; Wright & Bonett, 2002), supervisor attributes such

as supervisory support and leader-member exchange (for meta-analyses, see Ilies,

Nahrgang, & Morgeson, 2007; LePine et al., 2002), and situational factors such as role

stressors, organizational politics, and workplace climate (for meta-analyses, see Carr,

Schmidt, Ford, & DeShon, 2003; Chang, Rosen, & Levy, 2009; Eatough, Chang,

8

Miloslavic, & Johnson, 2011). In summary, despite the rich literature on predictors and

correlates of job performance, fairly sparse research has examined the role of

mindfulness in job performance, with few exceptions, as discussed below.

Mindfulness and Job Performance

As discussed above, trait mindfulness is construed as the average frequency with

which an individual experiences state mindfulness, or a state of consciousness

characterized by nonjudgmental awareness of and attention to present moment

experiences, over time. Mindfulness-based practices are defined as techniques aiming to

cultivate moment-by-moment state mindfulness. My review intends to cover extant

organizational research linking job performance to both naturally occurring trait

mindfulness and mindfulness-based practices that aim to foster mindfulness. Table 1

summarizes extant correlational and intervention studies of mindfulness and job

performance.

Correlational studies. Several extant studies have linked trait mindfulness to job

performance using field surveys. Dane and Brummel (2014) surveyed restaurant servers

and found that self-reported trait mindfulness was positively related to supervisor rating

of overall service performance, after taking into account servers’ work engagement.

Likewise, Reb et al. (2015) surveyed working adults in heterogeneous jobs in Singapore

and found a positive relationship between self-reported awareness at work and lagged

supervisor rating of task performance. In their survey in a health care setting, Beach et al.

(2013) found that health care professionals high in trait mindfulness, compared with

those low in trait mindfulness, received more favorable patient ratings of communication

quality and patient overall satisfaction. However, findings showing no link also exist.

9

Giluk (2010) surveyed university employees and found that self-reported trait

mindfulness was not related to concurrent or lagged supervisor ratings of job

performance. Other scholars suggest that the association between trait mindfulness and

job performance exists under certain boundary conditions. Zhang et al. (2013) surveyed

power plant workers in China and found that the presence component of trait mindfulness

was positively related to supervisor ratings of task performance only for those in jobs

with high task complexity. Shao and Skarlicki (2009) found that trait mindfulness was

positively related to academic performance among female, but not male, MBA students.

Dane’s (2011) conceptual work is germane because he suggests that the relationship

between mindfulness and task performance depends on the direction (internal vs.

external) of mindful attention, task environment, and task expertise; mindful attention to

environmental stimuli facilitates task performance in a dynamic (vs. static) task

environment whereas mindful attention to internal stimuli facilitates task performance for

individuals with a high level of task expertise.

Intervention studies. A few studies linked mindfulness interventions to job

performance. Grepmair et al. (2007) conducted a mindfulness intervention of

psychotherapists-in-training with pre- and post-tests. Participants received a routine

medical training during the first nine weeks, followed by nine weeks of daily Zen

meditation of focused breathing in seated posture (one hour per day). Compared with

regular training period, participants in the meditation period evidenced better therapeutic

performance indicated by greater reduction of physical and psychological symptoms of

their patients. In the quasi-experiment of university employees, Giluk (2010) found that

participants who opted into the 8-week Mindfulness-Based Stress Reduction or

10

Mindfulness-Based Cognitive Therapy group showed higher supervisor rating of

performance at 4-week follow-up (but not immediately after training) compared with

those choosing not to practice meditation. In a field experiment of middle managers,

Shonin, Gordon, Dunn, Singh, & Griffiths (2014) suggest that, compared with an active

control of cognitive-behavioral theories education, 8-week mindfulness awareness

training led to higher improvements of job performance.

In summary, past research using field surveys reveals an equivocal link between

trait mindfulness and job performance and offered a limited few boundary conditions for

a positive relationship between mindfulness and job performance. Similarly, the

underlying mechanisms linking mindfulness to job performance are largely unknown.

Thus, theory must be developed to determine the key pathways by which mindfulness

may result in improved performance and the conditions under which this is more or less

likely.

Methodologically, past intervention studies, despite varying mindfulness practices

employed, tend to suggest that mindfulness intervention is beneficial for job

performance. Nonetheless, those studies bear important limitations. Research designs that

use a single treatment group without control group or allow participants to self-select into

conditions fall short in making strong causal inferences of the performance effect of

mindfulness interventions (Shadish et al., 2002). Comparing mindfulness intervention

with a nonactive control group (e.g., waitlist or work as usual) does not provide strong

confidence in concluding that any observed effect is attributable to the content of

mindfulness intervention rather than the process of carrying out the intervention (Cook &

Campbell, 1979). A mindfulness intervention with strong design entails randomization of

11

participants and inclusion of a carefully chosen active control condition (Goyal et al.,

2014). Therefore, it is a timely endeavor to rigorously investigate the role of mindfulness

in job performance.

In this dissertation, I focus on job performance in service settings. Service settings

constitute a dynamic task environment involving interactions with clients. Individuals

with a higher level of mindfulness are capable of directing heightened attention to service

requests from clients and mobilize cognitive, physical, and affective resources to perform

service tasks. Supporting evidence comes from the correlational study suggesting that

clinicians higher in trait mindfulness showed better communication quality with patients

(Beach et al., 2013) and the field intervention suggesting that mindful awareness training

led to higher job performance, compared with active control (Shonin et al., 2014).

Hypothesis 1: Compared with active control, mindfulness intervention

leads to higher job performance.

12

Chapter 3: Mechanisms

In this section, I propose that a mindfulness intervention promotes client-focused

behaviors, in terms of attentiveness, perspective taking, and response flexibility, which,

in turn, translates into favorable job performance in service settings. I focus on client-

focused behaviors as interpersonal mechanisms because they are at the heart of successful

performance in service settings. As Ryan and Ployhart (2003) noted, “a focus on the

customer has become a major component of organizational strategies, regardless of

whether the organization is in the service or manufacturing sector” (p. 377).

Mediating Effect of Attentiveness

Drawing on the interpersonal communication and therapeutic literatures (Cegala,

Savage, Brunner, & Conrad, 1982; Duncan, Coatsworth, & Greenberg, 2009; Egan,

2014), I conceptualize attentiveness in service settings as the act of paying attention to

clients’ verbal and nonverbal communicative behaviors. Often viewed as an integral

component of listening, attentiveness has been of interest to researchers in a wide variety

of interpersonal settings, including social interaction (Bodie, 2011; Cegala et al., 1982),

parent-child interaction (Duncan et al., 2009), and physician-patient interaction (Egan,

2014). For example, in their study on interaction between parents and their children,

Duncan et al. (2009) highlighted parents’ paying full attention as behaviors of accurately

noticing children’s verbal and behavioral cues. Similarly, paying attention to patients’

verbal and nonverbal cues has been an integrative component of active listening training

among health care professionals (Egan, 2014).

Paying attention is at the heart of mindfulness and mindfulness practices. Kabat-

Zinn (2003, p. 145) describes mindfulness as “the awareness that emerges through paying

13

attention on purpose, in the present moment, and non-judgmentally to the unfolding of

experience moment by moment.” Some scholars explicitly view mindfulness meditation

as attention training (Jha, Krompinger, & Baime, 2007). I propose that a mindfulness

intervention promotes employees’ attentiveness to clients during encounters because

mindfulness meditation facilitates intentional awareness of, and attention to, present

moment events and experiences, including clients’ verbal and behavioral cues, as they

unfold during interpersonal interactions. In line with this argument, a recent review of

neuroscience research on mindfulness meditation reveals that mindfulness meditation

produces improvement in attentional functioning and sustained attentional engagement

(Malinowski, 2013). A recent meta-analysis (Sedlmeier et al., 2012) confirms that

mindfulness meditation facilitates attention compared with control groups. Short-term (7

hours over 7 weeks) mindfulness meditation guards against attention wandering in

university students (Morrison, Goolsarran, Rogers, & Jha, 2014; Mrazek, Franklin,

Phillips, Baird, & Schooler, 2013). Indeed, physicians undergoing mindfulness training

report that they become “attentive and listen deeply” to their patients (Beckman et al.,

2012, p. 4).

Attentiveness is beneficial for job performance because paying attention during

service encounters channels cognitive, affective, and physical resources toward

performing tasks and allows individuals to fully engage in providing excellent services

(Beal, Weiss, Barros, & MacDermid, 2005; Rich, LePine, & Crawford, 2010; Schaufeli,

Salanova, & González-Romá, & Bakker, 2002). In summary, I propose that, compared

with active control, mindfulness intervention promotes higher level of attentiveness,

14

which, in turn, leads to higher job performance. Because other client-focused

mechanisms are discussed, I propose attentiveness as a partial mediator.

Hypothesis 2: Compared with active control, mindfulness intervention

leads to higher job performance and the effect is partially mediated by

attentiveness.

Mediating Effect of Perspective Taking

Perspective taking, or adopting another person’s point of view (Parker & Axtell,

2001), has long been regarded as a key factor in social relationships (e.g., Batson, 1991).

Organizational research has suggested that perspective taking is associated with higher

supervisor ratings of contextual performance (Parker & Axtell, 2001) and role-defined

organizational citizenship behaviors (Kamdar, McAllister, & Turban, 2006). Perspective

taking also strengthens the relationship between intrinsic motivation and creativity (Grant

& Berry, 2011) and the relationship between team diversity and team creativity (Hoever,

van Knippenberg, van Ginkel, & Barkema, 2012). Different approaches to

conceptualizing perspective taking exist. Perspective taking has been viewed as a stable,

dispositional attribute (e.g., Kamdar et al., 2006), a cognitive process subject to

motivational or situational influences (e.g., Grant & Berry, 2011), a multiphasic

experiential process as in the clinical literature (Duan & Hill, 1996; Egan, 2014), or some

combination of these approaches (e.g., Parker & Axtell, 2001). I followed the second and

third approaches and viewed perspective taking as understanding clients’ thoughts and

feelings and therefore subject to situational influences such as intervention. It is

noteworthy that perspective taking is related to, yet distinct from, empathy. Empathy

comprises cognitive and affective components; cognitive empathy involves taking others’

15

perspectives while affective empathy relates to emotional concern and caring of others

(Vachon, Lynam, & Johnson, 2014).

Mindfulness and mindfulness practices are conducive for an individual to put

oneself in others’ shoes because mindfulness practices promote clarity and understanding

of one’s own mind and inner state, which serves as the scaffolding for deep

understanding of others’ thoughts, feelings, and experiences (Cozolino, 2006; Glomb et

al., 2011; Siegel, 2007). Mindfulness practices facilitate deep awareness and knowledge

of internal states through disentangling the self from experiences and reducing

automaticity (Glomb et al., 2011).

Individuals have the innate tendency to use the self as the reference point in

perceiving, evaluating, and reacting to internal and external stimuli. As a primary

appraisal process, individuals consider an event or experience starting with the questions

of whether it is relevant to my needs, beneficial for my well-being, and congruent with

my goals (Lazarus, 1991). Despite its clear benefits for survival, self-referential

processing can paradoxically cloud insights in one’s own mind. For example, positive

emotions arise as one perceives events as advantageous to oneself (Weiss & Cropanzano,

1996). Charged with positive affect, heuristic processing ensues which can curb deep

awareness and understanding of one’s thoughts and internal states (e.g., Forgas, 1995).

Mindfulness facilitates decoupling self from events and experiences by allowing a

receptive, nonjudgmental stance (Brown et al., 2007; Glomb et al., 2011). With

mindfulness, an individual perceives present moment thoughts, feelings, and events as

mental objects rather than what they mean to one’s self (Feldman, Greeson, & Senville,

2010; Shapiro, Carlson, Astin, & Freedman, 2006). Correlational evidence suggests that

16

dispositional mindfulness is negatively related the tendency to feel shame, a self-

conscious emotion (Woods & Proeve, 2014) and evidence from intervention research

suggests that long-term mindfulness training alters the structure of brain region

responsible for self-relevant processing (H lzel et al., 2011).

Mindfulness promotes clarity of one’s mind also through reduced automaticity.

Automaticity derives, in part, from consistent mental processes that reoccur in daily

routines (Bargh, 1994). Despite greater mental efficiency, automaticity obviates

conscious intention of acknowledging what is unfolding moment by moment. As Glomb

et al. (2011) noted, “automaticity has the unfortunate consequence of restricting

individuals’ perceptions and experiences of the present moment” (p. 126). Mindfulness

disrupts automaticity by bringing back attention to present moment experiences.

Research suggests that mindfulness activates the middle prefrontal cortex of the brain

(Siegel, 2007) and evokes metacognition that features a conscious mind over one’s

thoughts and feelings moment by moment without judgment (e.g., Cahn & Polich, 2006;

Jankowski & Holas, 2014); such cognitions foster deep insights in one’s internal states.

With deep understanding of one’s own thoughts and feelings resulting from

mindful decoupling of self and reduced automaticity, individuals are freed to be aware of

and understand others’ thoughts and feelings as if they occur to oneself. Indeed, research

suggests that participants in the Mindfulness-Based Stress Reduction program evidenced

increase in taking others’ perspectives (Birnie, Speca, & Carlson, 2010; Block-Lerner,

Adair, Plumb, Rhatigan, & Orsillo, 2007; Krasner et al., 2009). Of particular relevance

for this context, qualitative and quantitative evidence has converged to indicate that

mindfulness training promotes client-oriented attitudes and behaviors in service settings

17

(Irving et al., 2014; Krasner et al., 2009). For example, a mindfulness training has

produced improvements perspective taking among physicians (Krasner et al., 2009).

Seeing the world through clients’ eyes is beneficial for job performance in service

encounters. With deep understanding of clients’ needs, thoughts, and feelings, individuals

are at the vantage point to show care and concern for clients (e.g., Parker & Axtell, 2001)

and provide solutions most useful to clients (e.g., Grant & Berry, 2011), therefore

producing superior job performance. In summary, I propose that, compared with active

control, mindfulness intervention promotes higher level of perspective taking, which, in

turn, leads to higher job performance. Because other client-focused mechanisms are

discussed, I propose perspective taking as a partial mediator.

Hypothesis 3: Compared with active control, mindfulness intervention

leads to higher job performance and the effect is partially mediated by

perspective taking.

Mediating Effect of Response Flexibility

Response flexibility is defined as the capacity to “pause before taking verbal or

physical action” (Glomb et al., 2011, p. 129). Response flexibility involves overriding

impulsive reactivity by inserting a pause between stimuli and response, considering a

range of possible options, and initiating action in line with one’s goals and situational

demands (Glomb et al., 2011; Siegel, 2007). As Glomb et al. (2011) posited, mindfulness

promotes response flexibility by facilitating decoupling of the self, reduced automaticity,

and physiological regulation. Decoupling and reduced automaticity allow greater

awareness of one’s thoughts, feelings, and impulses of reacting to the situation and pose a

mental brake to slow down before reaction. The gap between stimuli and reaction is

18

valuable because it allows time and space for individuals to consciously consider the

range of possible alternatives and act in a way that best suits one’s needs and goals

(Siegel, 2007). Better physiological regulation resulting from mindfulness also

contributes to flexible responding because mindfulness down regulates physiological

arousal under stimuli that would otherwise impede one’s capacity to consider alternative

reactions (Cozolino, 2006). Qualitative research suggests that mindfulness meditation

facilitates response flexibility (Beckman et al., 2012). For example, individuals working

in interpersonal settings report that, after mindfulness meditation, they “slow down” and

“come to some wisdom” before answering (Glomb et al., 2011, p. 129). A recent review

also suggests that mindfulness training curbs reactivity and promotes response flexibility

(Davis & Hayes, 2011).

Response flexibility, in turn, facilitates job performance. In service settings,

response flexibility allows thoughtful consideration of the range of response options in an

encounter and initiation of the optimal reaction. The mental process of thinking and

choosing the appropriate behaviors in service encounters contributes to the technical core

of fulfilling one’s duty (Rotundo & Sackett, 2002). Response flexibility is also crucial for

job performance in stressful encounters; individuals refrain from impulsive reactions that

may exacerbate interpersonal conflict (Scott & Duffy, 2015) and choose the optimal

service solution among possible alternatives. In summary, I propose that, compared with

active control, mindfulness intervention promotes higher level of response flexibility,

which, in turn, leads to higher job performance. Because other client-focused

mechanisms are discussed, I propose response flexibility as a partial mediator.

19

Hypothesis 4: Compared with active control, mindfulness intervention

leads to higher job performance and the effect is partially mediated by

response flexibility.

20

Chapter 4: Boundary Conditions

In response to the recent call for understanding dispositional and organizational

factors that influence how individuals respond to workplace interventions (Tetrick &

Winslow, 2015), in this section, I propose trait agreeableness, perceived workgroup

service climate, and perceived work overload as dispositional, social, and job-related

contingencies of the mediated effect of mindfulness intervention on job performance.

Moderating Effect of Agreeableness

Agreeableness refers to the dispositional tendency involving altruism, caring and

rapport building (McCrae & John, 1992). Agreeableness is positively associated with

organizational citizenship behaviors (for a meta-analysis, see Chiaburu et al., 2011), job

satisfaction (for a meta-analysis, see Judge, Heller, & Mount, 2002), affective and

normative commitment (for a meta-analysis, see Choi, Oh, & Colbert, 2015), and

transformational leadership (for a meta-analysis, see Bono & Judge, 2004), and

negatively associated with unsafe work behaviors (for a meta-analysis, see Beus,

Dhanani, & McCord, 2015) and workplace deviance (for a meta-analysis, see Kluemper,

McLarty, & Bing, 2015). Agreeableness has been construed as an other-oriented

personality trait of attending to and concern for others’ needs and well-being (De Dreu

2006; De Dreu & Nauta, 2009; Grant & Wrzesniewski, 2010). As Organ and Lingl

(1995) noted, agreeableness involves “getting along with others in pleasant, satisfying

relationships” (p. 340).

Drawing on the aptitude-treatment interaction theory (Snow, 1991), individuals’

characteristics exert systematic influences on how they react to an intervention. For

example, medical research has focused on the effectiveness of a therapy as a function of

21

individuals’ psychological and biological attributes (e.g., Bockting et al., 2006; Stulz,

Kunzler, Barth, & Hepp, 2014). I propose that agreeableness should affect how

individuals respond to mindfulness intervention. Decades of research suggest that

personality shapes an individual’s motivation, which then directs attention and effort

toward work-related behaviors (Mitchell & Daniels, 2003). As an other-oriented

disposition, agreeableness motivates individuals to focus on information relating to

others’ needs, preferences, and well-being (De Dreu & Nauta, 2009) and engage in

maintaining positive interpersonal relationships (Graziano & Tobin, 2013). It is

reasonable to expect that a mindfulness intervention interacts with agreeableness in

fostering client-focused behaviors in terms of attentiveness, perspective taking, and

response flexibility. Individuals high on agreeableness may be more fertile ground for

mindfulness meditation because they are already dispositionally attuned to the other-

oriented mental and behavioral processes attributable to mindfulness training. Because

mindfulness meditation fosters heightened awareness of and attention to present moment

experiences without judgment (Kabat-Zinn, 1990), for individuals with a high level of

agreeableness, mindfulness training, coupled with the innate motive to be concerned for

others’ welfare and maintain positive relationships with others (e.g., Graziano & Tobin,

2013; Nettle, 2006), produces heightened attention to clients’ verbal and behavioral cues,

stronger effort in understanding clients’ needs and preferences, and more flexibility in

deriving thoughtful reactions to clients. On the contrary, for individuals low on

agreeableness, the client-focused behavioral processes of mindfulness meditation may be

hampered because individuals are less dispositionally motivated to attend to, understand,

and care for others.

22

Given that the client-focused behaviors that mindfulness meditation fosters—

attentiveness, perspective taking, and response flexibility—are expected to link to job

performance and that agreeableness is proposed to influence the effect of mindfulness

meditation on client-focused behaviors, I propose a first-stage moderated indirect effect

(Edwards & Lambert, 2007) of mindfulness intervention on job performance, as stated

below.

Hypothesis 5: Agreeableness moderates the mediated relationship

between mindfulness intervention and job performance (via attentiveness,

perspective taking, and response flexibility, respectively) such that the

indirect effect of mindfulness intervention via (a) attentiveness, (b)

perspective taking, and (c) response flexibility on job performance is

stronger for individuals higher in agreeableness.

Moderating Effect of Perceived Workgroup Service Climate

In the customer service literature, perceived workgroup service climate refers to

an individual’s perception that his or her workgroup emphasizes customer service quality

(Schneider, White, & Paul, 1998). An individual’s perception of service climate derives

from his or her observation and experience of workgroup procedures, practices, and

behaviors regarding the service quality that is expected, supported, and rewarded

(Schneider et al., 1998). Thus, perceived workgroup service climate reflects an

individual’s belief of expected service quality in one’s immediate work environment.

Drawing on norm activation theory (Schwartz, 1977), I propose that perceived

workgroup service climate influences the effect of mindfulness intervention on client-

focused behaviors through internalized obligation to provide quality services. The norm

23

activation theory posits that the awareness of situational demands and consequences of

actions fuels one’s moral obligation to behave toward the norm (Schwartz, 1977). Feeling

a strong service climate, an individual receiving mindfulness meditation would have

heightened awareness that quality service is expected, supported, and rewarded and

develop the obligation to perform well. Striving to provide quality service as prescribed

by the felt obligation, an individual pays attention to clients’ verbal and behavioral cues

without judgment, sees the life through clients’ eyes, and gives thoughtful consideration

before reacting to clients as these are inherently valued in the workgroup. On the

contrary, mindfulness meditation may be less effective for those who perceive lower

workgroup service climate because of weaker internalized obligation to provide quality

service.

Given that client-focused behaviors—attentiveness, perspective taking, and

response flexibility—that mindfulness meditation produces are expected to link to job

performance and that perceived workgroup service climate is expected to influence the

effect of mindfulness meditation on client-focused behaviors, I propose a first-stage

moderated indirect effect (Edwards & Lambert, 2007) of mindfulness intervention on job

performance, as stated below.

Hypothesis 6: Perceived workgroup service climate moderates the

mediated relationship between mindfulness intervention and job

performance (via attentiveness, perspective taking, and response

flexibility, respectively) such that the indirect effect of mindfulness

intervention via (a) attentiveness, (b) perspective taking, and (c) response

24

flexibility on job performance is stronger for individuals who perceive a

higher level of workgroup service climate.

Moderating Effect of Perceived Work Overload

Perceived work overload refers to individuals’ perceptions that “they are engaged

in fulfilling too many responsibilities or activities in light of the time available, their

abilities, and other constraints” (Parker, Johnson, Collins, & Nguyen, 2013. p. 872; see

also Rizzo, House, & Lirtzman, 1970). As an aspect of job demands (Demerouti, Bakker,

Nachreiner, & Schaufeli, 2001), work overload has been considered as a work stressor

and has been linked to increased stress, burnout (e.g., MacDonald, 2003), and work-to-

family conflict (for a meta-analysis, see Michel, Kotrba, Mitchelson, Clark, & Baltes,

2011), and decreased task performance and organizational citizenship behaviors (for a

meta-analysis, see Eatough et al., 2011).

I propose that perceived work overload influences the effect of mindfulness

intervention on client-focused behaviors. Individuals have limited attentional and

cognitive resources (Hobfoll, 1989); deploying these resources to one task tends to

diminish the resources allotted to competing tasks at the same moment (Beal et al., 2005).

According to Hockey (1997), work overload has implications for how individuals

allocate limited attentional resources to achieve performance goals. Under high workload,

individuals may adopt the performance protection strategy by focusing attention on

primary components of the task and diminish attentional focus on its peripheral

components (Hockey, 1997). As Hockey noted on performance under work overload,

“what is perceived as primary is protected, and everything else is dealt with only where

resources permit” (p. 84). Mindfulness meditation involves conscious self-regulation of

25

attention to internal and external experiences moment by moment without judgment

(Kabat-Zinn, 1990; Masicampo & Baumeister 2007). To the extent that work overload

narrows attentional focus on the core aspects of the job (e.g., accuracy and efficiency of

service), mindful awareness of and attention to the peripheral aspects of the job are

inhibited, hampering the self-regulatory effort in obtaining heightened attention to

clients’ verbal and behavioral cures, deep understanding clients’ thoughts and needs, and

thoughtful, flexible responses to clients. On the contrary, under low workload,

mindfulness meditation can mobilize attentional resources with full capacity to attend to,

understand, and react thoughtfully to clients. Disentangling service job performance into

core and peripheral tasks is key to understanding the role of perceived work overload.

Given that the client-focused behaviors—attentiveness, perspective taking, and

response flexibility—that mindfulness meditation fosters are expected to link to job

performance and that perceived work overload will influence the effect of mindfulness

meditation on client-focused behaviors, I propose a first-stage moderated indirect effect

(Edwards & Lambert, 2007) of mindfulness intervention on job performance, as stated

below.

Hypothesis 7: Perceived work overload moderates the mediated

relationship between mindfulness intervention and job performance (via

attentiveness, perspective taking, and response flexibility, respectively)

such that the indirect effect of mindfulness intervention via (a)

attentiveness, (b) perspective taking, and (c) response flexibility on job

performance is stronger for individuals who perceive a lower level of

work overload.

26

Chapter 5: Method

Overview

Participants were health care professionals in a health care organization in

California, United States. I conducted a pretest-posttest field experiment with

intervention (i.e., mindfulness meditation) and active control (i.e., wellness education)

conditions and with repeated measures from employee participants and patients over 15

days. The study included a preliminary phase with a background survey and an active

phase spanning three weeks. During weeks 1 to 3 (Monday to Friday each week, labeled

Day 1 to 15 in the study), participants performed two study tasks: (1) placed a personal

sticker provided by the researcher on a brief survey for each patient they assisted and (2)

completed a short online survey at the end of workday. During week 2 and 3 (Day 6 to

15), an additional study task was added; participants were randomized to conditions and

listened to a 10-minute audio about either (1) mindfulness meditation (treatment

condition) or (2) wellness education (active control condition) each morning prior to the

start of work in designated rooms at work. Figure 2 depicts the data collection scheme in

the active phase.

Sample

Seventy-eight health care professionals expressed interest in participating by

signing up online. Of those 78 indicating interest, 72 completed the background survey

and were enrolled in the study. Thirty-eight (53%) participants were in clinical positions

(e.g., medical assistant, psychotherapist, nurse practitioner) and thirty-four (47%) were in

administrative positions (e.g., patient intake coordinator, referrals specialist, human

resources specialist). Sixty-four participants worked full time (of which 48 worked with

27

patients face-to-face and 16 primarily over the phone) and eight participants worked part

time. Participants were predominantly Hispanic (61%) and White (21%). The distribution

of educational attainment was: graduate or professional degree (MBA, Master’s, Ph.D.,

etc.) (17%), some graduate school (1%), graduated from college (B.A., B.S., or other

Bachelor’s degree) (14%), some college or technical training beyond high school (1-3

years) (53%), graduated from high school or G.E.D. (14%), and some high school (grade

11 or less) (1%). Participants were 93% female, 54% married, and on average 34.0 years

old. On average, participants worked 4.8 years in the organization, 2.6 years in the

current job, 39.7 hours per week, and saw or assisted 22.7 patients in a typical workday.

Twenty-two percent were in supervisory positions.

Procedure

Recruitment. The Chief Executive Officer of the health care organization sent an

email to all employees inviting them to participate in the study. The total number of

employees receiving the email was unavailable. Embedded in the email was a link to the

consent form with detailed study information and sign up procedure. The study was

introduced as the “Worker Wellness Initiative” to examine whether different types of

wellness programs influence worker’s well-being and patient experience. It was made

clear that the study was being conducted by researchers from the University of

Minnesota, participation was completely voluntary, and choosing not to participate would

not affect their relations with their employer. At the start of recruitment, full-time

employees who worked with patients in person were deemed eligible and those who

worked part-time or worked with patients primarily over the phone were placed on the

28

waitlist depending on space availability. All waitlisted employees were eventually

included.

Preliminary and active study phases. In the preliminary phase of the study,

employees who signed up via web recruitment were invited to take a 15-20 minute online

background survey about their jobs, attitudes, behaviors, and characteristics.

During the week prior to the start of the active phase, I was stationed in the break

room to enable participants to check in with me during their breaks to receive a packet

containing instructions of daily activities during the active phase, operation manual of the

audio player, and a set of personal stickers with unique numbers printed on to be attached

to patient surveys.

After completion of the background survey and before the start of the 3-week

active phase (September 15 to October 3, 2014, Monday-Friday), each participant was

randomly assigned into the treatment (i.e., mindfulness meditation) or the active control

condition (i.e., wellness education) using a random number table. The treatment and

active control conditions each included 36 participants. During week 1 (Day 1 to 5),

patients to the clinic received a brief survey upon check in. Participants were asked to

place a personal sticker with a unique number (provided by the researcher) on the survey

for each patient they assisted. During week 1, participants also completed a short online

survey at the end of workday. These activities (patient sticker and end of day online

survey) continued through the study period (Day 1 to 15).

Patient survey procedure. Upon check-in at front desks in the clinic, each patient

received a packet of a short survey, a consent form, and a lottery ticket. Standard scripts

introducing the survey were provided to each front desk personnel. The patient survey,

29

printed on an 8.5” x 11” comment card, asked a patient to rate the first and second (if

any) participants that assisted the patient during the visit as well as general questions

about the visit. No patient identifying information was collected. Several participants

indicated that their patients did not need to go through front desk; they were asked to

distribute survey packets to their patients directly.

Patients rated the first two participants who placed a sticker on the survey.

Numbers printed on the stickers linked patient ratings to health care professionals in our

study. Patients returned completed surveys to one of the two locked collection boxes at

both exits of the clinic. Patients were blind to both the experimental condition the focal

health care professional was assigned to and his or her contents of intervention. I

collected patient surveys from the boxes periodically (at around 10am, noon, 2pm, 4pm,

and 6pm) each day and time stamped each survey. Patients who returned completed

surveys were entered into a drawing of a $25 cash prize for each day of the study day. So

as not to collect patient information, patients were asked to check the website for daily

winning numbers and contact me for the prize. The URL to the website of winning

numbers and contact information was printed on the lottery tickets that uniquely matched

with patient surveys by serial numbers.

A total of 2,018 patient surveys (1,390 in English and 628 in Spanish, two

encounters printed on each survey) were distributed during the three-week period and

1,501 surveys were collected with a response rate of 74.4% (1,055 English surveys

collected: 510 in week 1, 250 in week 2, 287 in week 3, and 8 on unclear date; 446

Spanish surveys collected: 197 in week 1, 123 in week 2, 124 in week 3, and 2 on unclear

date). 56 surveys had no stickers attached for either encounter, 38 surveys had at least

30

one sticker attached but no responses, and 7 additional surveys were discovered other

than in the collection boxes and could not be accurately time stamped. I obtained 1,400

usable patient surveys that had patient ratings for at least one encounter, which produced

patient ratings of 2,075 encounters with 54 health care professionals.

End-of-workday survey procedure. During the active study period (Days 1 to 15),

participants were sent a link to a short online survey each day approximately one hour

prior to the end of their workday, followed by a text message reminder (if they opted to

receive one). They were asked to complete the daily surveys right after finishing their

work and before leaving the workplace. The end-of-workday survey asked about

participants’ well-being and work experience during the day. On the days that

participants did not work, they only answered the questions about sleep, diet, and

exercise yesterday and then exited the survey. During the three-week period, participants

completed 882 of 1,080 daily surveys, yielding an 81.7% response rate. Of 882 surveys,

58 surveys were completed later than the noon of the next day and were removed from

analyses. I obtained 824 usable surveys, including 751 surveys completed on the same

day the survey was sent and 73 completed on the next day before noon (mostly 6am-

10am). Participants completed an average of 11.4 usable daily surveys over 15 days (SD

= 3.9).

Intervention procedure. During week 2 and 3 (Day 6 to 15), the treatment and

control condition activities began. Each day, upon arrival at work, participants checked in

with the researcher for an envelope containing an audio player and a pair of earbuds and

received room assignment. Depending on condition, participants listened to a 10-minute

audio about either (1) mindfulness meditation or (2) wellness education each morning

31

prior to the start of work in designated rooms at work (discussed below). Participants

entered designated rooms, quietly sat in a chair, and listened to the audio. Participants

were asked to return the equipment as soon as they finished the audio. I recorded the time

points that participants checked out and returned the equipment. For participants who

missed the session at the start of work on a given day, a makeup session was available

during the lunch break to listen to the audio content.

Immediately following the last daily survey on the Friday of week 3, participants

took a short online debrief survey asking for their reactions to the study. Participants who

completed at least 80% of daily study activities were compensated for a $100 cash card.

Those who completed fewer than 80% of daily study activities received prorated

payments. A debriefing handout was mailed to participants two weeks after the

conclusion of data collection.

Conditions

Intervention condition. The audio content in the intervention condition was based

on the sitting meditation module in the Mindfulness-Based Stress Reduction program

(MBSR; Kabat-Zinn, 1990). I adapted the audio from the sitting meditation audio CD

narrated by Jean Haley, MBSR Instructor, in the Center for Spirituality and Healing at the

University of Minnesota. The audio contains five 10-minute segments directing

participants to attend to breath, bodily sensations, sound, thoughts, and emotions,

respectively, in a nonjudgmental way. The audios were presented to each participant in a

random order in week 2 (Day 6 to 10) and again in week 3 (Day 11 to 15). The audios

were not described as “mindfulness” or “meditation” to participants.

32

Active control condition. Extant field experiments on mindfulness meditation

commonly adopt nonactive control conditions in which participants were waitlisted or

worked as usual (Aikens et al., 2014; Grepmair et al., 2007; Hülsheger et al., 2013;

Wolever et al., 2012). However, there are concerns as to whether such research designs

are able to conclude that the treatment condition actually worked (Cook & Campbell,

1979; Goyal et al., 2014). An active control condition to match the nonspecific elements

(e.g., length of sessions, audio delivery, and seated posture) of the treatment condition

allows for making stronger causal inferences that the effect of treatment is attributable to

the content rather than implementation of the intervention (Cook & Campbell, 1979;

Goyal et al., 2014). Thus, I selected an active control condition; specifically, wellness

education podcasts that presented scientific information on how to promote health and

well-being (for a similar approach, see Malarkey, Jarjoura, & Klatt, 2013). The active

control condition contains ten 10-minute health education audios on topics of nutrition,

exercise, and social relationships. Topics related to mindfulness or meditation were not

included. Eight audios were adapted from the “15 Minutes to Wellness” podcasts

(American Council on Exercise, 2014), one from the “Health and Longevity” podcast

(LifeTalk Radio, 2013), and one from the “Relationship Matters” podcast (Sage

Publications, 2012). Each participant listened to the audios in a random order in week 2

and 3 (Day 6 to 15). The audio sequences in both conditions were randomized to avoid

participants’ discussing the audio content and potentially guessing conditions. The audio

sequence for each participant was determined and logged prior to the start of active

phase. In each morning session, there was only one audio loaded in the player so

participants could not listen to additional audio content beyond the 10 minutes.

33

To minimize diffusion as a threat to internal validity (Cook & Campbell, 1979),

participants indicated agreement with a statement during enrollment that they would not

share information about their wellness activities with anyone in the organization (e.g.,

their supervisors, coworkers, and patients) for the duration of the study. At the conclusion

of data collection, I probed diffusion among participants with one question: “To what

extent were you aware of the wellness audio content for your coworkers?” (1 = Not at all,

4 = A lot). The average rating of 1.47 (SD = 0.80) suggests that diffusion was not a

concern.

Of the 10 audio segments, participants in the intervention condition attended an

average of 6.78 audios (SD = 3.57, ranging from 0 to 10) and those in the active control

condition attended an average of 6.81 (SD = 3.27, ranging from 0 to 10). Chi-square test

2 (10) = 6.65, p = .76).

Sixty-four participants (32 in the intervention condition and 32 in the active control

condition) attended at least one audio. Fifty-seven participants (27 in the intervention

condition and 30 in the active control condition) attended at least five (50%) audios.

Despite different audio contents in the intervention and active control conditions,

one may argue that both conditions could function similarly because their audios involve

information about certain aspects of well-being. Thus, it is important to verify that the

intervention condition effects changes in outcomes whereas the active control condition

does not. I focus on an array of work related outcomes that have shown, and are

expected, to be susceptible to mindfulness intervention. Research has suggested that,

compared with control group, mindfulness intervention promotes mindfulness (e.g.,

Hülsheger et al., 2013; for a meta-analysis, see Sedlmeier et al 2012) and reduces stress

34

(for a meta-analysis, see Sedlmeier et al., 2012), emotional exhaustion (e.g., Hülsheger et

al., 2013; Krasner et al., 2009), and fatigue (e.g., Zeidan, Johnson, Diamond, David, &

Goolkasian, 2010). Based on my theoretical model described in the previous sections,

mindfulness intervention should also promote performance related behaviors such as

client-focused behaviors (i.e., attentiveness, perspective taking, and response flexibility)

and proactive patient care. As detailed in the Results section, I examine whether the

intervention and active control conditions have differential impact on the above outcomes

using multilevel modeling and latent growth modeling. All variables assessed were

shown below.

Measures: Background Survey

Job autonomy was assessed using the revised three-item job autonomy measure

(Idaszak & Drasgow, 1987) from the Job Diagnostic Survey (Hackman & Oldham,

1975). Consistent with Grandey, Fisk, & Steiner (2005), responses were on an agreement

scale (1 = “strongly disagree” to 7 = “strongly agree”). A sample item was “The job gives

= .76).

Job satisfaction was measured using five items from the Brayfield Rothe scale

(Brayfield & Rothe, 1951). The five-item measure has been used in past research (e.g.,

Bono & Judge, 2003; Judge, Bono, & Locke, 2000). Responses were on an agreement

scale (1 = “strongly disagree” to 7 = “strongly agree”). Sample items were “I feel fairly

= .84).

35

Job stress was measured with two items from the four-item measure developed by

Motowidlo, Packard, and Manning (1986). Responses were on an agreement scale (1 =

“strongly disagree” to 7 = “strongly agree”). The two items were “I feel a great deal of

= .67).

Emotional exhaustion was measured using five items with the highest factor

loadings from the emotional exhaustion scale of the Maslach Burnout Inventory (Maslach

& Jackson, 1981). Responses were on an agreement scale (1 = “strongly disagree” to 7 =

“strongly agree”). Sample items were “I feel emotionally drained from my work” and “I

Supervisor support was measured by six high-loading items from the Perceived

Organizational Support measure (Eisenberger, Armeli, Rexwinkel, Lynch, & Rhoades,

2001). I modified items in reference to one’s immediate supervisor rather than the

organization. Responses were on an agreement scale (1 = “strongly disagree” to 7 =

“strongly agree”). Sample items were “My supervisor values my contributions” and “My

supervisor really cares about my well-

Coworker support was measured by six items from the short version of the

Inventory of Socially Supportive Behavior (Barrera, Sandler, & Ramsay, 1981), which

assesses perception of supportive behaviors and has been used in prior studies (e.g.,

Duffy, Scott, Shaw, Tepper, & Aquino, 2012). The selected items were relevant to the

health care setting. Respondents indicated how frequently their closest coworkers had

enacted these behaviors in the past month, using a six-point scale (1 = “never” to 6 =

36

“every day”). Sample items were “pitched in to help you do something that needed to be

Task self-efficacy was measured by four items adapted from the 8-item New

General Self-Efficacy Scale (Chen, Gully, & Eden, 2001). Items were selected based on

context relevance. “At work...” preceded each item. Responses were on an agreement

scale (1 = “strongly disagree” to 7 = “strongly agree”). Sample items were: “I am

confident that I can perform effectively on many different tasks” and “I will be able to

Core job performance was measured by four items with the stem “How often do

you…” using a seven-point frequency scale (1 = “never” to 7 = “always”). Two items

were from the in-role performance measure (Williams & Anderson, 1991): “perform the

tasks that are expected as part of your job” and “meet performance expectations.” The

other two items were adapted from Parker et al., (2013) to capture core job performance

specific to the medical context. The two items were “provide quality patient care or

assistance” and “provide timely patient care or assistance.” I added “or assistance”

because the sample also included healthcare professionals working at the front desk (e.g.,

receptionist, intake coordinator). I included “not applicable” as a response option for the

Proactive patient care was measured by two items from Johnson, Hong, Groth,

and Parkeret (2011) with the stem “How often do you…” using a seven-point frequency

scale (1 = “never” to 7 = “always”) with the additional “not applicable” option. The two

context specific items were “spend time thinking ahead to prevent possible

37

complications” and “spend time planning how a patient’s status and needs might change

Attentiveness was measured by six items adapted from the Interaction

Involvement Scale (Cegala et al., 1982) and the Active-Empathic Listening Scale (Bodie,

2011). I selected items of both global and specific statements of attentiveness and adapted

the wording to be relevant to the hospital context. Following the stem “During

interactions with patients…,” the items were “I listen carefully to patients,” “I am

sensitive to patients’ subtle meanings or cues,” “I ask questions that show my

understanding of patients’ situations,” “I find my mind wandering” (reverse scored), “I

show patients that I am listening by using verbal acknowledgements or body language

(e.g.., head nods),” and “I carefully observe how patients respond.” Responses were on

an agreement scale (1 = “strongly disagree” to 7 = “strongly agree”). As discussed below

in the confirmatory factor analysis section, the reverse scored item was uncorrelated with

the other items and had nonsignificant factor loading. Thus, it was removed from

Perspective taking was measured by four items adapted from the Davis, Conklin,

Smith, & Luce (1996) perspective taking measure. The measure has been used in past

research (e.g., Grant & Berry, 2011). The stem was “During interactions with patients…”

The items were “I frequently try to take patients’ perspectives,” “I regularly seek to

understand patients’ viewpoints,” “I make an effort to see the world through patients’

eyes,” and “I often imagine how patients are feeling.” Responses were on an agreement

scale

38

Response flexibility was measured by four items, using the stem “During

interactions with patients…” Two items were adapted from the Cognitive Flexibility

Inventory (Dennis & Vander Wal, 2010) to reflect the cognitive aspect of response

flexibility (i.e., pause to consider multiple options). The items were “I pause to consider

multiple options before responding to patients” and “I think of more than one way to

react to patients.” I developed two items to capture the behavioral aspect of response

flexibility (i.e., react based on conscious consideration). The items were “I respond to

patients after careful consideration” and “I react to patients based on their situations.”

Responses were on an agreement scale (1 = “strongly disagree” to 7 = “strongly agree”)

Perceived workgroup service climate was measured by 7 items adapted from the

Global Service Climate (Schneider et al., 1998) for health care settings, using a 5-point

scale (1 = “poor” to 5 = “excellent”). I adapted the items to capture an individual’s

perception of patient care climate within his or her team/department. The stem was “How

would you rate…” Sample items were “the overall quality of patient care and assistance

provided by your team/department” and “the job knowledge and skills of employees in

your team/department to del

Big five personality traits were measured by four-item scales from the Mini-IPIP

(Donnellan, Oswald, Baird, & Lucas, 2006), a widely used short measure of the big five

traits. Responses were on an agreement scale (1 = “strongly disagree” to 7 = “strongly

agree”). The stem was “I see myself as someone who…” Sample items were “is the life

39

Trait mindfulness was measured by the 15-item Mindful Attention Awareness

Scale (MAAS; Brown & Ryan, 2003), a widely used measure of dispositional

mindfulness with good psychometric properties (e.g., Hülsheger et al., 2013; Hülsheger et

al., 2014; Long & Christian, 2015). Respondents rated how frequently they had had these

experiences on a six-point scale (1 = “almost never” to 6 = “almost always”). Sample

items were “I do jobs or tasks automatically, without being aware of what I’m doing”

(reverse scored) and “I find myself doing things without paying attention” (reverse

scored). Higher composite score indicated higher dispositional

measurement validation study, Brown and Ryan (2003) correlated this measure with an

alternate measure in which items were positively worded (e.g., “I find it easy to stay

focused on what’s happening in the present”). The two measures correlated at .70. They

showed similar pattern of correlations with an array of personality, self-esteem,

optimism, anxiety, and depression scales. Thus, the MAAS measures mindfulness rather

than lack of mindfulness.

Mental health was measured by the 12-item abbreviated version of the Mental

Health Index (Veit & Ware, 1983). The measure focuses on the symptoms of distress

(i.e., depression and anxiety) and has been used in the general population (e.g., Lim,

Cortina, & Magley, 2008). Respondents indicated how frequently they had experienced

the symptoms over the past three months on a 5-point response scale (1 = “never” to 5 =

“most of the time”). Sample items were “felt tense or high strung” (reverse scored) and

40

“felt depressed” (reverse scored). Higher composite score indicated better mental health

Overall health was measured by one item adapted from DeSalvo et al. (2006),

“Over the last three months, would you say your health is…?” (1 = “poor” to 5 =

“excellent”). Single-item measure of general health has been used in past studies (e.g.,

Dahm, Glomb, Manchester, & Leroy, 2015).

Fatigue was measured by two items from the fatigue dimension of the Positive

and Negative Affect Schedule Expanded Form (PANAS-X; Watson & Clark, 1994).

Participants indicated how often they had felt “tired” and “sluggish” over the past three

months on a 5-

Participants also provided background information, including sex (1 = female, 0 =

male), age, educational attainment (1 = some high school (grade 11 or less, 2 = graduated

from high school or G.E.D., 3 = some college or technical training beyond high school

(1-3 years), 4 = graduated from college (B.A., B.S., or other Bachelor’s degree, 5 = some

graduate school, 6 = graduate or professional degree (MBA, Master’s, Ph.D., J.D., etc),

ethnic background (1 = White, 0 = other), current marital status (1 = married, same-sex

domestic partner, or living with a significant other or partner, 0 = single, divorced,

separated, or widowed), job tenure, job title, clinical position (1 = clinical position, 0 =

administrative position), workgroup affiliation, supervisory position (1 = yes, 0 = no),

average work hours per week, and average number of patients see or assist per day.

Measures: End-of-Workday Survey

Work overload was measured by three items from the five-item perceived role

overload measure (Parker et al., 2013). The selected items had the highest factor loadings

41

and were relevant to the hospital context (Parker et al., 2013). The response scale was

anchored at 1 = “not at all” and 5 = “very much.” The items were “Today at work, did

you have to work faster than you would like to complete your work,” “Today at work, did

you have too much work to do in too little time,” and “Today at work, how much

Job autonomy was measured by one item modified from Trougakos, Hideg,

Cheng, & Beal (2014) lunch break autonomy measure. The item was closest to the

conceptual definition of perceived autonomy and was suitable to the hospital context.

Participants rated the item, “Today at work, did you feel like you decided for yourself

what to do,” using a 5-point scale (1 = “not at all” to 5 = “to a very large extent”).

Daily mindfulness was measured by three items from the 5-item state mindfulness

measure (Brown & Ryan, 2003) on a 5-point response scale (1 = “not at all” to 5 = “very

much”). The state measure has been used in past studies to capture daily mindfulness at

work (e.g., Hülsheger et al., 2013; Hülsheger et al., 2014). The selected items had the

highest loadings (Hülsheger et al., 2014; Brown & Ryan, 2003). The items were “Today

at work, I found myself doing things without paying attention,” “Today at work, I did

jobs or tasks automatically, without being aware of what I was doing,” and “Today at

work, I rushed through activities without being really attentive to them” (all reversed

Core job performance assessed daily global and specific job performance in

reference to an individual’s personal average. I used three items adapted from the core

job performance measure in the background survey, using a 5-point scale, 1 = “far below

my personal average” to 5 = “far above my personal average” (for a similar approach, see

42

Miner & Glomb, 2010). The items were “Today at work, the quality of care or assistance

I provided has been…,” “Today at work, the timeliness of care or assistance I provided

has been…,” and “T

Proactive patient care was measured by one item from the proactive patient care

measure in the background survey, using a 5-point response scale (1 = “never” to 5 =

“always”) with the “not applicable” option. The item was “Today at work, how often did

you spend time planning how a patient’s status and needs might change over time.”

Attentiveness was measured by two items that captured the global concept from

the attentiveness measure as described in the background survey. The responses were on

a 5-point scale (1 = “never” to 5 = “always”). The items were “Today at work, I listened

carefully to patients,” and “Today at work, I was sensitive to patients’ subtle meanings or

Perspective taking was measured by two items from the perspective taking

measure as described in the background survey, using a 5-point response scale (1 =

“never” to 5 = “always”). The items were “Today at work, I made an effort to see the

world through patients’ eyes,” and “Today at work, I tried to take patients’ perspectives”

Response flexibility was measured by two items that most closely captured the

construct from the response flexibility measure as described in the background survey,

using a 5-point response scale (1 = “never” to 5 = “always”). The items were “Today at

work, I paused to consider multiple options before responding to patients,” and “Today at

work, I thought of more than one way to react to patients” ).

43

Stress was measured by one item adapted from the single-item stress measure in

Beal, Trougakos, Weiss, and Dalal (2013) and Bono, Glomb, Shen, Kim, and Koch

(2013). The item captured the global concept using a 5-point scale (1 = “not at all” to 5 =

“very much”). The item was “Today at work, how stressed did you feel.”

Mood was measured by a single item from Fuller et al., (2003) to capture general

mood each day. The item was “Today at work, I was in a good mood,” using a 5-point

response scale (1 = “not at all” to 5 = “very much”).

Job satisfaction was measured by a single item of global job satisfaction (Fuller et

al., 2003), “Today, I was satisfied with my job.” The responses were on a 5-point scale (1

= “not at all” to 5 = “very much”). A past meta-analysis supported the validity of one-

item job satisfaction measures (Wanous, Reichers, & Hudy, 1997).

Emotional exhaustion was measured by a single item that closely captured the

construct from the Maslach Burnout Inventory (Maslach & Jackson, 1981). The item was

“Today, I felt emotionally drained from my work” with a 5-point response scale (1 =

“strongly disagree” to 5 = “strongly agree”).

Fatigue was measured by a single item from the PANAS-X (Watson & Clark,

1994), “Right now, do you feel tired,” using a 5-point scale (1 = “not at all” to 5 = “to a

very large extent”). Past experience sampling research has used the measure to assess

end-of-workday fatigue (e.g., Trougakos et al., 2014).

Sleep quality was measured by a single item from the Westerberg et al., (2010)

sleep quality measure. The item was “How well did you sleep last night,” using a 5-point

response scale (1 = “very poorly” to 5 = “very well”).

44

Diet was measured by one item that I wrote, “How nutritious and healthy were

your meals and snacks yesterday (during the day and evening),” using a 5-point scale (1 =

“not at all” to 5 = “very much”).

Exercise was measured by one item that I wrote, “How physically active

(walking, biking, exercising) were you yesterday (during the day and evening),” using a

5-point scale (1 = “not at all” to 5 = “very much”).

In the end-of-workday surveys, participants also indicated what shift they worked,

hours worked, and the number of patients they saw or assisted each day.

Measures: Patient Survey

Our discussions with clinic management reveled that a considerable portion of the

patient population was Spanish speaking. Thus, the patient packet was available in

English and Spanish versions. Following Brislin (1980), two professional translators

independently translated the English version into Spanish. A third more experienced

translator reviewed both Spanish versions against the English version and edited them

into a best Spanish version. A fourth translator independently back translated the best

Spanish version into English. The resulting English version was almost identical to the

original. Minor discrepancies were discussed by all translators and the researcher and

consensual translation was obtained.

Patient satisfaction, as patient perception of participants’ job performance, was

measured by five items. I used four items from the Visit-Specific Satisfaction

Questionnaire (VSQ-9; Rubin et al., 1993) to assess patients’ global satisfaction and

satisfaction with specific aspects (i.e., technical skills, personal manner, and explanation)

of the quality of care. The VSQ-9 has been a widely used patient satisfaction measure in

45

the medical literature (e.g., Rubin et al., 1993; Chai-Coetzer et al., 2013; Frank, Stange,

Langa, & Workings, 1997). I adapted the items to capture a patient’s satisfaction with

performance of the focal health care professional who had provided him/her with care or

assistance rather than with the visit. I included an additional item to capture satisfaction

with the timeliness of care such that the targets of patient evaluation matched the

conceptual space of core job performance (i.e., quality care, timely care, and overall

performance) as perceived by participants themselves. Patients rated their satisfaction

with the health care professional they had just interacted with on a 5-point scale, 1 = “not

at all satisfied” to 5 = “very satisfied” (Cooper-Patrick et al., 1999; Frank et al., 1997).

Following the stem, “How satisfied are you with…,” the items were “the visit with this

person overall,” “the technical skills (thoroughness, carefulness, competence) of this

person,” “the personal manner (courtesy, respect, sensitivity, friendliness) of this

person,” “this person’s explanation of what was done for you,” and “the timeliness of

Perceived attentiveness was measured by three items adapted from the

attentiveness measure as described in the background survey. Two of the original items

have the highest loadings in the background survey. Patients rated their perceptions of the

focal health care professional’s behaviors using a 7-point scale (1 = “strongly disagree”

to 7 = “strongly agree”). The items were “This person listened carefully to me,” “This

person was sensitive to my subtle meanings or cues,” and “This person carefully

observe

Perceived perspective taking was measured by three items adapted from the

highest loading items in the perspective taking measure described in the background

46

survey. Patients rated their perceptions of the focal health care professional’s behaviors

using a 7-point scale, 1 = “strongly disagree” to 7 = “strongly agree.” The items were

“This person tried to take my perspective,” “This person made an effort to see the world

through my eyes,” and “This person sought to understand m

Perceived response flexibility was measured by three items adapted from the

highest loading items in the response flexibility measure described in the background

survey. Patients rated their perceptions of the focal health care professional’s behaviors

using a 7-point scale, 1 = “strongly disagree” to 7 = “strongly agree.” The items were

“This person paused to consider multiple options before responding to me,” ”This person

responded to me after careful consideration,” and “This person thought of more than one

For each health care professional, patients also indicated whether they had been

treated by this person in the past (yes or no) and the time they spent with this person (in

minutes). Patients also provided the total time they spent at the clinic (in munities), the

reason for the visit, overall health, and sex.

47

Chapter 6: Results

Overview

I performed analyses in several ways. First, I performed a series of confirmatory

factor analyses of the patient interaction items for attentiveness, perspective taking, and

response flexibility in the participant background survey, participant end-of-workday

survey, and patient survey. The three constructs were found to be represented by a

higher-order construct of patient centered behavior, which was robust at participant-,

day-, patient-, and encounter-levels across surveys. Second, I reported descriptive

statistics and zero-order correlations of study variables in the background survey, end-of-

workday survey, and patient survey. I also reported the pre- and post- intervention means

and standard deviations of each outcome by condition. Third, I examined the effect of

intervention for each outcome using (1) multilevel modeling to examine whether

treatment and active control led to differential post-intervention means across participants

for each outcome (including patient ratings of job performance which depicts the main

effect of mindfulness intervention in Hypothesis 1), and (2) latent growth modeling to

examine whether treatment and active control produced differential growth trajectories of

each outcome during intervention. Both analyses were necessary because they looked at

the effects of the intervention and active conditions from different angles. Fourth, I used

cross-classified multilevel moderated mediation modeling to examine the effect of

experimental condition (via patient perception of patient centered behavior) on patient

perception of job performance (i.e., patient satisfaction) and the moderating effects of

participant agreeableness, perceived workgroup patient care climate, and daily work

overload. All analyses were performed using Mplus 7.11 (Muthén & Muthén, 2012).

48

Confirmatory Factor Analysis of Patient Interaction Items

Participant background survey. I performed confirmatory factory analysis on the

attentiveness, perspective taking, and response flexibility items from 72 participants in

the background survey. The item “I find my mind wandering” (reverse scored) was

unrelated to any other attentiveness item and was removed from subsequent analyses.

Maximum likelihood estimation was used by default in Mplus for single-level analysis.

There was no missing data across items. A three-factor model in which items loaded on

attentiveness, perspective 2 = 149.17, df = 62,

p < .001, Root Mean Square Error of Approximation [RMSEA] = .14, Standardized Root

Mean Square Residual [SRMR] = .06, CFI = .88) was compared against two alternative

models. The first alternative was a one-factor model with all items loaded on a single

factor 2 = 175.49, df = 65, p < .001, RMSEA = .15, SRMR = .07, CFI =.84). The

second alternative was a higher-order-factor model in which items loaded separately on

the three factors which 2 = 149.17, df = 62, p

< .001, RMSEA = .14, SRMR = .06, CFI = .88). The three-factor model fit significantly

better than the one- 2 (3) = 26.32, p < .001) and fit equally well as the

higher-order-factor model. For parsimony, a single-factor model is preferred when it

showed similar fit to a multi-factor model (Bollen, 1989). Thus, the higher-order-factor

model was appropriate. The latent factors of attentiveness, perspective taking, and

response flexibility showed significant loadings on the higher-

= .996, .857, and .964, respectively, all ps < .001). All item loadings on respective factors

were also significant. The standardized item loadings are shown in Table 2.

49

Participant end-of-workday survey. The data structure of self-reported patient

interaction items in the end-of-workday survey was day nested within participant. I

followed the procedures recommended by Dyer, Hanges, and Hall (2005) to validate the

two-level data structure. First, in the conventional confirmatory factor analyses (not

considering the nested data structure), a three-factor model in which items loaded on

2 = 66.54, df = 6, p

< .001, RMSEA = .11, SRMR = .01, CFI = .99) was compared against two alternative

models. The first alternative was a one-factor model with all items loaded on a single

2 = 322.30, df = 9, p < .001, RMSEA = .21, SRMR = .04, CFI =.93). The second

alternative was a higher-order-factor model in which items loaded separately on the three

2 = 66.54, df = 6, p < .001,

RMSEA = .11, SRMR = .01, CFI = .99). The three-factor model fit significantly better

than the one- 2 (3) = 255.76, p < .001) and fit equally well as the higher-

order-factor model. Thus, the parsimonious higher-order-factor model was appropriate,

confirming findings in the background survey data with a greater number of items.

Muthén’s (1994) ICCs for the six patient interaction items ranged from .60 to .67, with an

average of .64, suggesting sufficient between-participant variation. Thus, multilevel

confirmatory factor analysis was warranted (Dyer et al., 2005). I expected that the higher-

order-factor structure would be consistent at both levels and performed confirmatory

factor analysis of higher-order-factor models simultaneously at the within- and between-

participant levels. To avoid negative residual variances, I fixed to zero the between-

participant level residual variances of items “Today at work, I paused to consider

multiple options before responding to patients” and “Today at work, I tried to take

50

patients’ perspectives” and the within-participant level residual variance of response

flexibility factor (Dyer et al., 2005). Robust maximum likelihood estimation was used by

default in Mplus for multilevel analysis. The higher-order-factor model yielded

acceptable fit at the day and participant 2 = 73.48, df = 15, p < .001, RMSEA

= .07, CFI =.95, SRMRbetween = .01, SRMRwithin = .04) (see Hirst et al., 2009; Hirst et al

2011, for empirical examples of multilevel confirmatory factor analysis). All items had

significant loadings on respective latent factors. The standardized item loadings at the

within- and between-participant levels are shown in Table 3.

Patient survey. The patient interaction items in patient survey had a complex data

structure; encounters were nested within days nested within participants and encounters

were also nested within patients. To quantify the amount of variance for each item at each

level, I started with three-level variance decomposition with encounter nested within day

nested within participant. The average of within-level variance estimates across items

was 0.83 (range = 0.50 to 1.03, all ps < .001), the average of day-level variance estimates

across items was 0.01 (range = 0.003 to 0.025, ps > .10 except p < .10 for two items), and

the average of participant-level variance estimates across items was 0.02 (range = 0.009

to 0.031, p < .001 for one item, p < .01 for four items, p < .05 for two items, p < .10 for

one item, and p > .10 for one item). Because of nonsignificant variance at day level

across items, day was not considered a meaningful cluster. Next, I partitioned variance of

each item based on cross-classified data structure with encounters nested simultaneously

within participant and patient. Bayesian estimation with noninformative priors for

parameters was used by default in Mplus for cross-classified analysis (Asparouhov &

Muthén, 2010; Muthén & Asparouhov, 2012). The average of within-level variance

51

estimates across items was 0.39 (range = 0.29 to 0.45, all 95% credibility intervals

excluded zero), the average of patient-level variance was 0.46 (range = 0.22 to 0.55, all

95% credibility intervals excluded zero), and the average of participant-level variance

was 0.02 (range = 0.01 to 0.05, all 95% credibility intervals excluded zero). Thus,

participant and patient were both meaningful clusters.

For confirmatory factor analysis with cross-classified data, I first performed a

series of conventional confirmatory factor analysis using sample total covariance matrix

(not considering nesting) to determine the optimal factor structure of patient interaction

items. Next, I expected that the optimal factor structure held at within-, patient-, and

participant-level and performed confirmatory factor analysis at each level simultaneously

using level-specific covariance matrices (Meyers & Beretvas, 2006).

In the conventional confirmatory factor analyses, I constructed a three-factor

model of patient rated attentiveness, perspective taking, and response flexibility with

items loading separately. The initial model yielded a non-positive definite latent

covariance matrix, suggesting that improvement be made in model specification. The

largest modification index occurred for the residual covariance between two items, “This

person tried to take my perspective,” and “This person was sensitive to my subtle

meanings or cues,” indicating that model fit would improve with this residual covariance

freed. Indeed, the two items had the highest Pearson correlation coefficient of .85 among

all patient rated items. A close look at the patient survey questionnaire revealed that,

despite randomization of item order, these two items were adjacent to each other in both

blocks (one block for the first health care professional the patient interacted with and the

other for the second), leading to the possibility that patients might respond to these items

52

in a similar fashion due to their positional proximity. Thus, it was justifiable to specify a

correlated error between them (Byrne, 1998). The modified three-factor model provided

2 = 495.93, df = 23, p < .001, RMSEA = .10, SRMR = .02, CFI = .98). I

tested the three-factor model against two alternative models: (1) a single-factor model

2 = 759.13, df = 27, p < .001, RMSEA = .12,

SRMR = .02, CFI = .96), and (2) a higher-order-factor model with items loading

separately on attentiveness, perspective taking, and response flexibility which then loaded

on a higher-order factor. In the higher-order factor model, to avoid non-positive

covariance matrix of latent variables, I freed the residual covariance between the same

two items as in the three-factor model. In the next (and final) model, I fixed the residual

variance of the latent factor of perspective taking to zero avoid a nonsignificant negative

2 = 496.67, df = 24, p < .001, RMSEA = .10, SRMR = .02, CFI = .98). The

three-factor model significantly improved model fit than the single- 2 (4)

= 263.20, p < .001) but did not significantly improve fit than the higher-order factor

2 (1) = 0.74, p > .10). These findings suggested that the more parsimonious

higher-order-factor model was appropriate. All item loadings were statistically significant

(ps < .001).

I then performed cross-classified confirmatory factor analysis of the higher-order-

factor model at within-, participant, and patient-levels. Bayesian estimation with 150,000

iterations and noninformative priors was used. Partial missing data was handled by

Bayesian estimation. To check for convergence, increasing the number of iterations to

300,000 did not change results. Fit statistics for Bayesian cross-classified models (e.g.,

posterior predictive p-values, deviance [DIC]) were not available in Mplus 7.11. At each

53

level, item loadings on attentiveness, perspective taking, and response flexibility factors

were all high and significant and the three factors loaded significantly on a higher-order

factor. Thus, the higher-order-factor model of patient interaction items was appropriate at

within-, participant-, and patient-levels. The standardized item loadings at each level are

shown in Table 4.

Summary. A series of confirmatory factor analyses of patient interaction items in

employee background survey, employee daily survey, and patient survey converged to

suggest that the measurement model in which attentiveness, perspective taking, and

response flexibility loaded on a higher-order factor consistently provided excellent fit

across levels. Thus, in the subsequent analyses, I focused on the patient interaction

composite (termed patient centered behavior) by aggregating items into a single scale

rather than attentiveness, perspective taking, and response flexibility separately.

Description of Study Variables

The means, standard deviations, and zero-order correlations of study variables in

the participant background survey are shown in Table 5. Experimental condition was

unrelated to all variables except fatigue (r = –.27, p < .05), suggesting random

assignment of participants was successful. As expected, patient centered behavior was

positively correlated with core job performance (r = .32, p < .01) and proactive patient

care (r = .31, p < .01). The means, standard deviations, and zero-order correlations of

participant-reported daily variables in the end-of-workday survey during the baseline and

intervention periods (Day 1 to 15) are shown in Table 6. As expected, daily patient

centered behavior was positively related to daily job performance (r = .32, p < .001) and

proactive patient care (r = .49, p < .001). The means, standard deviations, and zero-order

54

correlations of patient-reported variables in the patient survey during baseline and

intervention periods (Day 1 to 15) are shown in Table 7. As expected, patient perception

of participants’ patient centered behavior was positively related to encounter-specific

patient satisfaction (r = .49, p < .001). Table 8 showed the pre- and post-intervention

means and standard deviations for study variables by condition.

Effect of Intervention on Participant and Patient Outcomes

Analytic strategies

I examined the impact of the treatment and active control intervention on an array

of work related outcomes over the 10-day intervention period. The work related

outcomes included participant self-reported daily mindfulness, job satisfaction, work

behaviors (i.e., job performance, proactive patient care, patient centered behavior), and

stress-related outcomes (i.e., stress, emotional exhaustion, fatigue). The work related

outcomes also included patient ratings of the encounter-specific participants’ patient

centered behavior and patient satisfaction.

For each outcome, I examined whether the treatment and active control conditions

had differential impact on the outcome using multilevel modeling and latent growth

modeling, as detailed below. I first performed intent-to-treat analysis (Shadish et al.,

2002) using participants who attended at least one audio over the 10-day intervention

period. Participants’ self-reported daily outcome was useable if it was responded to on

the same day a daily survey was sent or by noon the following day. Because intent-to-

treat analysis revealed the effect of being assigned to a condition rather than actually

listening to audios, I then replicated the analyses using participants who attended at least

five (50%) of audios.

55

Multilevel modeling. For participant reported daily outcomes, given the data

structure of day nested within participant, I used multilevel random intercept modeling

(Hox, 2010; Raudenbush & Bryk, 2002; Snijders & Bosker, 2012) to examine the effect

of experimental condition as a participant-level predictor on daily outcome over

intervention period, provided that the outcome had a significant amount of variance at the

between-participant level. First, variance decomposition of daily outcome was performed

by estimating a null model without predictors at either the participant level or the day

level. ICC(1), or participant-level variance divided by the sum of variance at the

participant- and day-level, indicated the proportion of total variance at the participant

level (Bliese, 2000). Next, I used a random intercept model to capture the extent to which

variability in participants’ person-averaged daily outcome was accounted for by the

experimental condition to which they were assigned. I controlled for the outcome at

baseline, computed as the average of daily measures over the 5-day pre-intervention

period for each participant. Daily observations with missing data on the outcome were

removed from analyses by default in Mplus. Robust maximum likelihood estimation as

the default in Mplus was used to produce standard errors that were robust to non-

normality and non-independence of observations (Muthén & Muthén, 2012; Preacher,

Zyphur, & Zhang, 2010).

Because participants attended varying numbers of audios in total over the

intervention period, I then explored the potential impact of total number of audios

attended on the effect of experimental condition on the outcome. This was achieved by

including total number of audios attended and its product term with experimental

condition as additional participant-level predictors in the random intercept model. In

56

cases of significant interaction effect, simple slope tests (Aiken & West, 1991) were

performed for the conditional effect of experimental condition on the outcome at high

and low levels (i.e., 1 SD above and below the mean) of total number of audios attended.

For patient rated encounter-specific outcomes (i.e., patient centered behavior and

patient satisfaction), cross-classified multilevel modeling (e.g., Hox, 2010) was used to

take into account encounter nested within the cross-classification of participant and

patient, given that each outcome had nonsignificant variance at the day level. The effect

of experimental condition on participant-level variability of each outcome was modeled

using Bayesian estimation by default in Mplus.

Latent growth modeling. I performed multiple-group latent growth modeling

(Bollen & Curran, 2006; Lipsey & Cordray, 2000) to examine whether the treatment and

active control conditions produced differential growth trajectories of a daily outcome

over the 10-day intervention period. Although multilevel modeling and latent growth

modeling both deal with repeated measures of an outcome, multilevel modeling answers

the question of whether participants’ means of repeated measures of an outcome differ

between the treatment and active control conditions, whereas latent growth modeling

addresses between-condition difference in the developmental trajectory of an outcome

over time (Preacher, Wichman, MacCallum, & Briggs, 2008; Wright, Hallquist, Swartz,

Frank, & Cyranowski, 2014). In their seminal work, Muthén and Curran (1997)

integrated multiple-group latent growth modeling in randomized intervention research to

model the effect of an intervention on growth of outcome over time (for an empirical

example, see Dekovic, Asscher, Manders, Prins, & van der Laan, 2012). The tenet of

Muthén and Curran’s (1997) approach is to decompose the observed growth trajectory of

57

repeated measures of an outcome over intervention period into (1) the normative growth

trajectory that would have been observed had participants not received any intervention

and (2) the growth trajectory attributable to intervention. One way to identify the

normative growth trajectory was using repeated measures of an outcome over the course

of intervention from participants in a randomly assigned nonactive control group that

received no treatment (Muthén & Curran, 1997).

As recommended by Muthén and Curran (1997), I proceeded with the following

steps. First, I modeled normative developmental trajectory of an outcome using repeated

measures over the five days before the intervention started. The normative trajectory

captured potential influence of all other study activities (i.e., patient survey handoff, end-

of-workday survey) on the outcome except that of audio intervention. Given that

participants in both conditions engaged in the same study activities except listening to

audio throughout all days before and during intervention, it was appropriate to treat the

trajectory prior to the start of intervention as the normative trajectory over intervention

period, reflecting the otherwise observed development of outcome without either the

intervention or active control. Participants in both conditions were included because the

normative development would not differ across conditions due to randomization of

participants. I expected the normative development of each outcome to be a flat trajectory

over study period. In latent growth modeling, the flat trajectory was represented by a

latent initial status factor without any latent growth (i.e., rate of change) factor. The initial

status factor had loadings of 1.0 on 5 days of the outcome before intervention. The flat

trajectory was compared with a linear growth trajectory. As typical specifications of

linear growth model (Bollen & Curran, 2006), the intercept factor loadings of the

58

outcome on Day 1 to Day 5 prior to intervention were fixed at 1.0 and the growth factor

loadings were fixed at 0, 1, 2, 3, and 4 such that one unit of time was one day. The

variances and covariance of the intercept and growth factors were freely estimated. The

residuals of the outcome over five days were allowed to have unequal variances and be

uncorrelated. Partial missing data were handled by maximum likelihood estimation by

default in Mplus to produce unbiased parameter estimates under the assumption of

missing at random (MAR; Graham, 2009). The expected flat trajectory can be obtained

by fixing the growth factor mean and variance (therefore its covariance with initial status

factor) to zero. Chi-square difference test was used for model comparison. For patient

ratings of patient centered behavior and patient satisfaction, daily patient rated outcome

was computed by averaging patient ratings across encounters on each day for each

participant. As discussed below, all of the daily outcomes showed flat normative

development during days prior to intervention. Thus, flat trajectory was adopted for all

outcomes in the subsequent analyses.

Second, to assess the fit of linear growth model, latent growth modeling with

linear form was performed separately on participants in the treatment and active control

conditions, using baseline outcome (computed as the averaged outcome over 5 days prior

to intervention) and 10 days of repeated measures of the outcome during intervention.

The initial status factor had loadings of 1.0 on all time points and the slope factor had

loadings of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 on baseline and Day 1 to Day 10 during

intervention (Bollen & Curran, 2006). Therefore, one unit of time was defined as one

day.

59

Third, two-group latent growth modeling (Muthén & Curran, 1997; Preacher et

al., 2008) was performed simultaneously on participants in the treatment and active

control conditions, using baseline and 10 days of repeated measures. As depicted in

Figure 3, in each condition, daily observations were determined by normative

development (i.e., initial status factor) and linear increase or decline specific to the

intervention (i.e., linear slope factor). The initial status factor had loadings of 1.0 on all

time points to represent flat normative development and the slope factor had loadings of 0

on baseline and 1 through 10 on Day 1 through Day 10 to represent linear growth specific

to intervention. One unit of time was defined as one day. Although my hypotheses

focused on the average level of treatment and active control effects across participants,

intervention effect can also be characterized by the extent to which it varied across

participants and covaried with initial status before intervention started (Muthén & Curran,

1997). Thus, in each condition, I allowed the intervention growth factor variance and its

covariance with initial status to be freely estimated. In the two-group analysis, equality

constraints were applied to the parameters (i.e., mean and variance) of initial status factor

across conditions to represent the common normative development. However, parameters

(i.e., mean, variance, and covariance) of the linear growth factor were freely estimated in

each condition to reflect the realism of treatment and active control effects.

Fourth, I examined whether linear growth factor specific to treatment was

different from that specific to active control. In the two-group model from step 3, means,

variances, and covariances (with initial status) of linear growth factors were constrained

to be equal across conditions, respectively. The chi-square of model fit was compared

with the step 3 model. Significant increase in chi-square in step 4 would indicate that the

60

step 3 model showed better fit and would suggest that treatment effect was different from

active control effect.

Lastly, given that random assignment may be influenced by attrition of

participants, I examined whether baseline outcome was different across conditions. In the

two-group model in step 3, I relaxed equality constraints on mean and variance of initial

status factors across conditions. For each outcome (except proactive patient care),

relaxing equality constraints on initial status parameters did not significantly improve

model fit, suggesting that the outcome at baseline was not different across conditions and

that randomization was not affected by attrition.

In supplemental analyses in the Appendix, to reduce model complexity, I repeated

steps 2 to 4 using five repeated measures of an outcome during intervention by averaging

measures on two adjacent days (i.e., wave 1 measure as the averaged outcome on Day 1

and 2, wave 2 as the averaged outcome on Day 3 and 4, and so forth) (for a similar

approach, see Van Iddekinge et al., 2009). As a robustness check for the aggregated

measures, analyses were replicated separately using measures on Day 1, 3, 5, 7, and 9 and

Day 2, 4, 6, 8, and 10 during intervention (Z. Zhang, May 2015, personal

communication).

Because the treatment and active control effects may be subject to the extent to

which participants received intervention, as supplemental analyses, I reran latent growth

modeling analyses described above using a constrained sample of participants who

attended at least 50% (five) audios. Below I provided a summary of findings, followed by

detailed results for each outcome.

61

Summary of findings

Table 9 summarizes the findings for participants attending at least one audio.

Multilevel modeling suggests that participants’ means of daily proactive patient care and

patient ratings of patient centered behavior in the treatment condition during intervention

was higher than that in the active control condition for participants attending a higher

(rather than lower) number of audios. For all other day- and encounter-level outcomes,

participants’ means of repeated measures of the outcome during intervention did not

differ between the treatment and active control conditions, regardless of total number of

audios attended.

Latent growth modeling of 10 days of repeated measures suggests that treatment

and active control conditions produced distinguishable growth trajectories for

mindfulness, job performance, proactive patient care, and patient satisfaction. The

treatment condition, rather than active control, yielded significant increases in self-

reported mindfulness and job performance over the intervention period. However, growth

trajectories of proactive patient care and patient satisfaction failed to reach significance in

the treatment and active control conditions. Although the treatment condition yielded

significant increase in self-reported patient centered behavior and decline in fatigue over

time, the trajectories were not significantly distinguishable from those in the active

control condition.

Taken together of the findings from multilevel modeling and latent growth

modeling, compared with active control, treatment yielded pre-to-post increases in

mindfulness and work related behaviors in terms of self-reported job performance and

proactive patient care and patient ratings of patient centered behavior.

62

Mindfulness

Multilevel modeling. Variance decomposition of daily mindfulness over

intervention period suggested significant between- 00 = 0.34, p

< .01) and within- 2 = 0.34, p < .001); 50% of total variance resided

between participants. Thus, multilevel modeling was warranted. As shown in Model 1 in

Table 10, exper –.01, p > .10),

after controlling for baseline mindfulness. In Model 2 in Table 10, the interaction term of

= .01, p > .10), suggesting that total number of audios attended did not influence the

effect of experimental condition on daily mindfulness.

Latent growth modeling. First, normative development of daily mindfulness as

2 (10) = 8.05, RMSEA < .001, CFI = 1.00) did not significantly improve

2 2 (3) =

5.46, p = .14. Thus, the overall normative development of daily mindfulness among

participants was a flat trajectory, as shown in Table 20. Second, as shown in Table 21,

linear growth model yielded modest fit in the treatment and active control groups

separat 2 2 (63) =

181.23, RMSEA = .24, CFI = .57). As shown in Table 22, growth factor mean was

positive and significant in the treatment condition (mean slope = 0.02, p < .05) and was

nonsignificant in the active control condition (mean slope = 0.001, p > .10). Third, in the

two-group latent growth modeling with equality constraints on initial status parameters

2 (126) = 332.76,

RMSEA = .23, CFI = .59, treatment growth factor mean was positive and significant

63

(mean slope = .02, p < .05) whereas active control growth factor mean was not significant

(mean slope = .002, p > .10), as shown in Table 25. Results suggested that treatment led

to a 0.02-unit increase of mindfulness each day over intervention period after taking into

account normative development, whereas active control did not produce change in

mindfulness over time. Fourth, the two-group analysis with additional equality

2 (127) = 337.81, RMSEA

2 (1) = 5.05, p = .02. Thus, the

treatment effect was different from active control effect.

Summary. Multilevel modeling suggests that participants’ means of daily

mindfulness during intervention did not differ between the treatment and active control

conditions, regardless of total number of audios attended. Latent growth modeling

suggests that treatment, rather than active control, produced increase in daily mindfulness

over time. The treatment and active control trajectories were distinguishable based on 10

days of repeated measures.

64

Job satisfaction

Multilevel modeling. Variance decomposition of daily job satisfaction over

intervention period suggested significant between- 00 = 0.39, p

< .001) and within- 2 = 0.55, p < .001); 41% of total variance

resided between participants. Thus, multilevel modeling was warranted. As shown in

Model 1 in Table 11

= .13, p > .10), after controlling for baseline job satisfaction. In Model 2 in Table 11, the

interaction term of condition and total number of audios attended was unrelated to daily

–.04, p > .10), suggesting that total number of audios attended did

not influence the effect of experimental condition on daily job satisfaction.

Latent growth modeling. First, normative development of daily job satisfaction as

2 (10) = 13.71, RMSEA = .08, CFI = .96) did not significantly improve

2 2 (3) =

5.79, p > .10. Thus, the overall normative development of daily job satisfaction among

participants was a flat trajectory, as shown in Table 20. Second, as shown in Table 21,

linear growth model yielded modest fit in the treatment and active control groups

2 (61) = 183.43, RMSEA = . 2 (61) =

176.58, RMSEA = .24, CFI = .36). As shown in Table 22, growth factor mean was not

significant in either the treatment or active control condition (mean slope = .02 and .001,

respectively, ps > .10). Third, in the two-group latent growth modeling with equality

constraints on initial status parameters across conditions and free parameters of

2 (124) = 360.29, RMSEA = .24, CFI = .47, treatment and

active control growth factor means were not significant (mean slope = .02 and .001,

65

respectively, ps > .10), as shown in Table 26. Results suggested that neither treatment nor

active control produced change of job satisfaction over time. Fourth, the two-group

analysis with additional equality constraints on parameters of growth factors across

2 (127) = 363.66, RMSEA = .24, CFI = .47) did not significantly worsen

2 (3) = 3.37, p > .10. Thus, the treatment effect was not different from active

control effect.

Summary. Multilevel modeling suggests that participants’ means of daily job

satisfaction during intervention did not differ between the treatment and active control

conditions, regardless of total number of audios attended. Latent growth modeling

suggests that neither treatment nor active control produced change in daily job

satisfaction over time. The treatment and active control trajectories were not

distinguishable based on 10 days of repeated measures.

66

Job performance

Multilevel modeling. Variance decomposition of daily job performance over

intervention period suggested significant between- 00 = 0.27, p

< .001) and within- 2 = 0.21, p < .001); 56% of total variance

resided between participants. Thus, multilevel modeling was warranted. As shown in

Model 1 in Table 12

= .15, p > .10), after controlling for baseline job performance. In Model 2 in Table 12, the

interaction term of condition and total number of audios attended was unrelated to daily

–.05, p > .10), suggesting that total number of audios attended did

not influence the effect of experimental condition on daily job performance.

Latent growth modeling. First, normative development of daily job performance

2 (12) = 15.58, RMSEA = .07, CFI = .97) significantly improved model

2 2 (1) = 6.75, p

< .01. Thus, the average normative development of daily job performance among

participants was a flat trajectory, although individuals’ growth trajectories vary, as shown

in Table 20. Second, as shown in Table 21, linear growth model yielded modest fit in the

treatment and active 2 (61) = 136.60, RMSEA

2 (61) = 238.54, RMSEA = .30, CFI = .38). As shown in

Table 22, growth factor mean was positive and significant in the treatment condition

(mean slope = .03, p < .01) but was not significant in the active control condition (mean

slope = .004, p > .10). Third, in the two-group latent growth modeling with equality

constraints on initial status parameters across conditions and free parameters of

intervention growt 2 (124) = 377.60, RMSEA = .25, CFI = .57, treatment

67

growth factor mean was positive and significant (mean slope = .03, p < .01) whereas

active control growth factor mean was not significant (mean slope = .003, p > .10), as

shown in Table 27. Results suggested that treatment produced a 0.03-unit increase of job

performance each day while active control failed to produce change of job performance

over time. Fourth, the two-group analysis with additional equality constraints on

parameters of growth 2 (127) = 385.30, RMSEA = .25, CFI

2 (3) = 7.7, p = .05. Thus, the treatment effect

was different from active control effect.

Summary. Multilevel modeling suggests that participants’ means of daily job

performance during intervention did not differ between the treatment and active control

conditions, regardless of total number of audios attended. Latent growth modeling

suggests that treatment, rather than active control, produced increase in daily job

performance over time. The treatment and active control trajectories were distinguishable

based on 10 days of repeated measures.

68

Proactive patient care

Multilevel modeling. Variance decomposition of self-reported daily proactive

patient care over intervention period suggested significant between-participant variance

00 = 0.74, p < .001) and within- 2 = 0.37, p < .001); 67% of total

variance resided between participants. Thus, multilevel modeling was warranted. As

shown in Model 1 in Table 13, experimental condition was unrelated to daily proactive

p > .10), after controlling for baseline proactive patient care. In

Model 2 in Table 13, the interaction term of condition and total number of audios

p < .05), suggesting that total

number of audios attended influenced the effect of experimental condition on daily

proactive patient care. Simple slope tests indicated that experimental condition was

positively related to proactive patient care for participants attending a high level (1 SD

above the mean) of total audios (b = .49, p < .05) but was unrelated to proactive patient

care for those attending a low level (1 SD below the mean) of total audios (b = –.16,

p > .10). As shown in Figure 4, compared with active control, treatment led to a higher

level of proactive patient care for participants attending a higher number of audios.

Latent growth modeling. First, normative development of daily proactive patient

2 (10) = 19.26, RMSEA = .13, CFI = .90) did not significantly

2 (13) = 23.44, RMSEA = .12, CFI = .89),

2 (3) = 4.18, p > .10. Thus, the overall normative development of daily proactive

patient care among participants was a flat trajectory, as shown in Table 20. Second, as

shown in Table 21, linear growth model yielded modest fit in the treatment and active

2 (61) = 161.98, RMSEA = .24, CFI = .65; active

69

2 (61) = 263.72, RMSEA = .34, CFI = .47). As shown in Table 22, growth factor

mean was not significant in either the treatment or active control condition (mean slope

= .02 and –.004, respectively, ps > .10). Third, in the two-group latent growth modeling

with equality constraints on initial status parameters across conditions and free

2 (124) = 431.73, RMSEA = .29, CFI = .54,

treatment and active control growth factor means were not significant (mean slope = .02

and –.01, ps > .10), as shown in Table 28. Results suggested that neither treatment nor

active control produced change in proactive patient care over time. Fourth, the two-group

analysis with additional equality constraints on parameters of growth factors across

2 (127) = 440.28, RMSEA = .29, CFI = .53) significantly worsened model

2 (3) = 8.55, p < .05. Thus, the treatment effect was different from active control

effect.

Summary. Multilevel modeling suggests that participants’ means of daily

proactive patient care in the treatment condition was higher than that in the active control

condition for participants attending a higher (rather than lower) amount of audios. Latent

growth modeling suggests that neither treatment nor active control produced change in

daily proactive patient care over time. Nonetheless, the treatment and active control

trajectories were distinguishable based on 10 days of repeated measures.

70

Participant-rated patient centered behavior

Multilevel modeling. Variance decomposition of daily participant-rated patient

centered behavior over intervention period suggested significant between-participant

00 = 0.61, p < .001) and within-part 2 = 0.17, p < .001); 78%

of total variance resided between participants. Thus, multilevel modeling was warranted.

As shown in Model 1 in Table 14, experimental condition was unrelated to daily

participant-rated patient centered behavior p > .10), after controlling for baseline

participant-rated patient centered behavior. In Model 2 in Table 14, the interaction term

of condition and total number of audios attended was unrelated to daily participant-rated

patient centered behavior p > .10), suggesting that total number of audios

attended did not influence the effect of experimental condition on daily participant-rated

patient centered behavior.

Latent growth modeling. First, normative development of daily participant-rated

2 (12) = 7.50, RMSEA < .001, CFI = 1.00)

2 (13) = 16.80, RMSEA = .07,

2 (1) = 9.30, p < .01. Thus, the average normative development of daily

participant-rated patient centered behavior among participants was a flat trajectory,

although individuals’ growth trajectories vary, as shown in Table 20. Second, as shown in

Table 21, linear growth model yielded modest fit in the treatment and active control

2 (61) = 155.25, RMSEA = .22, CFI = .78; active control:

2 (61) = 138.24, RMSEA = .20, CFI = .85). As shown in Table 22, growth factor mean

was positive and significant in the treatment condition (mean slope = .03, p < .01) but

was not significant in the active control condition (mean slope = .01, p > .10). Third, in

71

the two-group latent growth modeling with equality constraints on initial status

parameters across conditions and free parameters of intervention 2 (124)

= 294.54, RMSEA = .21, CFI = .81, treatment growth factor mean was positive and

significant (mean slope = .03, p < .01) whereas active control growth factor mean was not

significant (mean slope = .01, p > .10), as shown in Table 29. Results suggested that

treatment led to a 0.03-unit increase of patient centered behavior each day while active

control did not produce change in patient centered behavior over time. Fourth, the two-

group analysis with additional equality constraints on parameters of growth factors across

2 (127) = 299.66, RMSEA = .21, CFI = .81) did not significantly worsen

2 (3) = 5.12, p = .16. Thus, the treatment effect was not different from active

control effect.

Summary. Multilevel modeling suggests that participants’ means of self-reported

daily patient centered behavior during intervention did not differ between the treatment

and active control conditions, regardless of total number of audios attended. Latent

growth modeling suggests that treatment, rather than active control, produced increase in

daily self-reported patient centered behavior over time. The treatment and active control

trajectories were not distinguishable based on 10 days of repeated measures.

72

Stress

Multilevel modeling. Variance decomposition of daily stress over intervention

period suggested significant between- 00 = 0.49, p < .001) and

within- 2 = 0.85, p < .001); 37% of total variance resided between

participants. Thus, multilevel modeling was warranted. As shown in Model 1 in Table 15,

–.13, p > .10), after controlling

for baseline stress. In Model 2 in Table 15, the interaction term of condition and total

nu p > .10), suggesting that

total number of audios attended did not influence the effect of experimental condition on

daily stress.

Latent growth modeling. First, normative development of daily stress as linear

2 (11) = 7.50, RMSEA = .09, CFI = .91) significantly improved model fit

2 2 (2) = 20.5, p

< .001. Thus, the overall normative development of daily stress among participants was a

flat trajectory with variability of growth rates among participants, as shown in Table 20.

Second, as shown in Table 21, linear growth model yielded good fit in the treatment

group and modest??? fit in the active control group separatel 2 (61) = 85.09,

2 (61) = 167.18, RMSEA = .23, CFI = .35). As

shown in Table 22, growth factor mean was not significant in the treatment and active

control conditions (mean slope = –.01 and –.01, ps > .10). Third, in the two-group latent

growth modeling with equality constraints on initial status parameters across conditions

2 (124) =252.87, RMSEA = .18, CFI

= .57, treatment and active control growth factor means were not significant (mean slope

73

= –.01 and –.001, ps > .10), as shown in Table 30. Results suggested that neither

treatment nor active control produced change in stress over time. Fourth, the two-group

analysis with additional equality constraints on parameters of growth factors across

2 (127) = 253.76, RMSEA = .18, CFI = .58) did not significantly worsen

2 (3) = 0.89, p > .10. Thus, the treatment effect was not different from active

control effect.

Summary. Multilevel modeling suggests that participants’ means of daily stress

during intervention did not differ between the treatment and active control conditions,

regardless of total number of audios attended. Latent growth modeling suggests that

neither treatment nor active control produced change in daily stress over time. The

treatment and active control trajectories were not distinguishable based on 10 days of

repeated measures.

74

Emotional exhaustion

Multilevel modeling. Variance decomposition of daily emotional exhaustion over

intervention period suggested significant between- 00 = 0.50, p

< .001) and within- 2 = 0.83, p < .001); 38% of total variance

resided between participants. Thus, multilevel modeling was warranted. As shown in

Model 1 in Table 16, experimental condition was unrelated to daily emotional exhaustion

–.17, p > .10), after controlling for baseline emotional exhaustion. In Model 2 in

Table 16, the interaction term of condition and total number of audios attended was

p > .10), suggesting that total number of

audios attended did not influence the effect of experimental condition on daily emotional

exhaustion.

Latent growth modeling. First, normative development of daily emotional

2 (12) = 8.47, RMSEA = .00, CFI = 1.00) significantly

2 (13) = 13.17, RMSEA = .01, CFI = 1.00),

2 (1) = 4.70, p < .05. Thus, the overall normative development of daily emotional

exhaustion among participants was a flat trajectory with variability of growth rates

among participants, as shown in Table 20. Second, as shown in Table 21, linear growth

model yielded modest fit in the treatment and active control groups separately (treatment:

2 2 (61) = 164.16, RMSEA

= .23, CFI = .49). As shown in Table 22, growth factor means were not significant in the

treatment and active control conditions (mean slope = –.02 and .01, ps > .10). Third, in

the two-group latent growth modeling with equality constraints on initial status

2 (124)

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=274.62, RMSEA = .20, CFI = .55, treatment and active control growth factor means

were not significant (mean slope = –.02 and .01, ps > .10), as shown in Table 31. Results

suggested that neither treatment nor active control produced change in emotional

exhaustion over time. Fourth, the two-group analysis with additional equality constraints

2 (127) = 280.56, RMSEA = .19, CFI

2 (3) = 5.94, p > .10. Thus, the treatment

effect was not different from active control effect.

Summary. Multilevel modeling suggests that participants’ means of daily

emotional exhaustion during intervention did not differ between the treatment and active

control conditions, regardless of total number of audios attended. Latent growth modeling

suggests that neither treatment nor active control produced change in daily emotional

exhaustion over time. The treatment and active control trajectories were not

distinguishable based on 10 days of repeated measures.

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Fatigue

Multilevel modeling. Variance decomposition of daily fatigue over intervention

period suggested significant between- 00 = 0.34, p < .001) and

within- 2 = 0.93, p < .001); 27% of total variance resided between

participants. Thus, multilevel modeling was warranted. As shown in Model 1 in Table 17,

–.17, p > .10), after controlling

for baseline fatigue. In Model 2 in Table 17, the interaction term of condition and total

p > .10), suggesting

that total number of audios attended did not influence the effect of experimental

condition on daily fatigue.

Latent growth modeling. First, normative development of daily fatigue as linear

2 (12) = 14.45, RMSEA = .06, CFI = .97) did not significantly improve model

2 2 (1) = 0.31,

p > .10. Thus, the overall normative development of daily fatigue among participants was

a flat trajectory, as shown in Table 20. Second, as shown in Table 21, linear growth

model yielded modest fit in the treatment and active control groups separately (treatment:

2 2 (61) = 157.76, RMSEA

= .22, CFI = .41). As shown in Table 22, growth factor mean was negative and significant

in the treatment condition (mean slope = –.05, p < .05) but was not significant in the

active control condition (mean slope = –.03, p > .10). Third, in the two-group latent

growth modeling with equality constraints on initial status parameters across conditions

2 (124) =290.25, RMSEA = .21, CFI

= .42, treatment growth factor mean was negative and significant (mean slope = –.05, p

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< .01) and active control growth factor mean was not significant (mean slope = –.02,

p > .10), as shown in Table 32. Results suggested that treatment led to a 0.05-unit

decrease of fatigue each day whereas active control produced no change in fatigue over

time. Fourth, the two-group analysis with additional equality constraints on parameters of

2 (127) = 291.54, RMSEA = .20, CFI = .42) did not

2 (3) = 1.29, p > .10. Thus, the treatment effect was not

different from active control effect.

Summary. Multilevel modeling suggests that participants’ means of daily fatigue

during intervention did not differ between the treatment and active control conditions,

regardless of total number of audios attended. Latent growth modeling suggests that

treatment, rather than active control, produced decline in daily fatigue over time. The

treatment and active control trajectories were not distinguishable based on 10 days of

repeated measures.

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Summary of participant self-reported outcomes

Multilevel modeling suggests that participants’ means of daily proactive patient

care in the treatment condition was higher than that in the active control condition for

participants attending a higher (rather than lower) amount of audios. For other self-

reported outcomes, participants’ means of the daily outcome during intervention did not

differ between the treatment and active control conditions, regardless of total number of

audios attended.

Latent growth modeling of 10 days of repeated measures suggests that treatment

and active control conditions produced distinguishable growth trajectories for

mindfulness, job performance, and proactive patient care. The treatment condition, rather

than active control, yielded significant increases in self-reported mindfulness and job

performance over the intervention period. However, growth trajectories of proactive

patient care failed to reach significance in the treatment and active control conditions.

Although the treatment condition yielded significant increase in self-reported patient

centered behavior and decline in fatigue over time, the trajectories were not significantly

distinguishable from those in the active control condition.

Below I examined the effect of intervention on patient rated outcomes (i.e.,

patient centered behavior and patient satisfaction).

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Patient-rated patient centered behavior

Multilevel modeling. The data structure of patient-rated patient centered behavior

over intervention period was complex; encounter was nested within day nested within

participant and encounter was also nested within patients. Although the nesting structure

has been examined for each item in the “confirmatory factor analysis” section above, it is

necessary to determine the appropriate variance structure for the composite. I first

performed three-level variance decomposition (i.e., encounter within day within

participant), yielding significant amounts of variance at the participant (.02, p = .06) and

encounter levels (.79, p < .001) but nonsignificant variance at the day level (.01, p > .10).

Thus, day was not treated as a meaningful cluster. Next, I partitioned variance using a

cross-classified data structure with encounter simultaneously nested within participant

and patient, yielding significant amounts of variance at the participant (variance = .02,

95% Bayesian Credibility Interval (CI) = [.01, .07]), patient (variance = .57, 95% CI =

[.49, .66]), and encounter (variance = .24, 95% CI = [.21, .29]) levels. 2.4% of total

variance resided between participants. Thus, cross-classified multilevel modeling was

warranted.

As shown in Model 1 in Table 18, experimental condition was unrelated to

patient- –.05, .20]), after controlling

for baseline patient-rated patient centered behavior. In Model 2 in Table 18, the

interaction term of condition and total number of audios attended was related to patient-

number of audios attended influenced the effect of experimental condition on patient-

rated patient centered behavior. Simple slope tests indicated that experimental condition

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was positively related to patient-rated patient centered behavior for participants attending

a high level (1 SD above the mean) of total audios (b = .20, 95% CI = [.04, .37]) but was

unrelated to patient-rated patient centered behavior for those attending a low level (1 SD

below the mean) of total audios (b = –.20, 95% CI = [–.51, .07]). As shown in Figure 5,

compared with active control, treatment led to a higher level of patient-rated patient

centered behavior for participants attending a higher number of audios.

Latent growth modeling. First, normative development of daily patient-rated

patient centered beh 2 (12) =11.33, RMSEA = .00, CFI = 1.00)

2 (13) = 11.36, RMSEA

2 (1) = 0.03, p > .10. Thus, the overall normative development of

daily patient-rated patient centered behavior among participants was a flat trajectory, as

shown in Table 20. Second, as shown in Table 21, linear growth model yielded poor fit in

2 (61) =208.26, RMSEA

= 2 (63) = 235.50, RMSEA = .33, CFI = .13). As shown in

Table 22, growth factor mean was not significant in the treatment or active control

condition (mean slope = .004 and .001, ps > .10). Third, in the two-group latent growth

modeling with equality constraints on initial status parameters across conditions and free

2 (126) = 445.64, RMSEA = .32, CFI = .09,

treatment and active control growth factor means were not significant (mean slope = .000

and .002, ps > .10), as shown in Table 33. Results suggested that neither treatment nor

active control produced change in patient rated patient centered behavior over time.

Fourth, the two-group analysis with additional equality constraints on parameters of

2 (129) = 446.49, RMSEA = .31, CFI = .10) did not

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2 (3) = 0.85, p > .10. Thus, the treatment effect was not

different from active control effect.

Summary. Participants’ means of patient ratings of patient centered behavior in

the treatment condition was higher than that in the active control condition for

participants attending a higher (rather than lower) amount of audios. Latent growth

modeling suggests that neither treatment nor active control produced change in daily

averages of patient ratings of patient centered behavior over time. The treatment and

active control trajectories were not distinguishable based on 10 days of repeated

measures.

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Patient satisfaction (main effect of the mindfulness intervention)

Multilevel modeling. Hypothesis 1 predicts that, compared with active control, a

mindfulness intervention leads to higher job performance. I focused on patient

satisfaction as patient ratings of job performance. The data structure of patient

satisfaction over intervention period was complex; encounter was nested within day

nested within participant while encounter was also nested within patients. To determine

the appropriate variance structure, I first performed three-level variance decomposition

(i.e., encounter within day within participant), yielding significant amounts of variance at

the encounter level (variance = .19, p < .001) but nonsignificant variance at the day

(variance = .002, p > .10) and participant levels (variance < .001, p > .10). Thus, day and

participant may not be meaningful clusters. Nonetheless, I partitioned variance using a

cross-classified data structure with encounter simultaneously nested within participant

and patient, yielding significant amounts of variance at the participant (variance = .002,

95% Bayesian Credibility Interval (CI) = [.001, .005]), patient (variance = .13, 95% CI =

[.11, .15]), and encounter (variance = .07, 95% CI = [.06, .09]) levels. 1.0% of total

variance resided between participants. Thus, cross-classified multilevel modeling was

warranted.

As shown in Model 1 in Table 19, experimental condition was unrelated to patient

–.02, .06]), after controlling for baseline patient

satisfaction. Thus, Hypothesis 1 was not supported. In Model 2 in Table 19, the

interaction term of condition and total number of audios attended was unrelated to patient

–.01, .06]), suggesting that total number of audios

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attended did not influence the effect of experimental condition on patient rated patient

satisfaction.

Latent growth modeling. First, normative development of daily patient

2 (12) =14.51, RMSEA = .07, CFI = .16) did not

sig 2 (13) = 15.29, RMSEA = .06,

2 (1) = 0.78, p > .10. Thus, the overall normative development of daily

patient satisfaction among participants was a flat trajectory, as shown in Table 20.

Second, as shown in Table 21, linear growth model yielded poor fit in the treatment and

2 (64) =203.19, RMSEA = .29, CFI = .00;

2 (63) = 188.46, RMSEA = .28, CFI = .00). As shown in Table 22, growth

factor mean was negative and significant in the treatment condition (mean slope = –.01, p

< .01) but was not significant in the active control condition (mean slope = .002, p > .10).

Third, in the two-group latent growth modeling with equality constraints on initial status

2 (127)

= 412.71, RMSEA = .30, CFI = .00, treatment and active control growth factor means

were not significant (mean slope = –.001 and –.01, ps > .10), as shown in Table 34.

Results suggested that neither treatment nor active control produced change in patient

satisfaction over time. Fourth, the two-group analysis with additional equality constraints

on parameters of growth factors across conditions 2 (129) = 419.21, RMSEA = .30, CFI

2 (2) = 6.50, p < .05. Thus, the treatment effect

was different from active control effect.

Summary. Multilevel modeling suggests that participants’ means of patient

satisfaction during intervention did not differ between the treatment and active control

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conditions, regardless of total number of audios attended. Latent growth modeling using

10 days of repeated measures suggests that neither treatment nor active control produced

change in daily averages of patient satisfaction over time, although the treatment and

active control trajectories were distinguishable.

Supplemental analyses

For each outcome above, I replicated the two-group analyses using 10 days and 5

waves of repeated measures, using a constrained sample of participants who attended at

least five (50%) audios during intervention. Same pattern of results as using the intent-to-

treat sample were obtained for each outcome, as shown in Table 35.

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Cross-Classified Multilevel Moderated Mediation Analyses: Analytic Strategies

Hypotheses 2 to 4 predict that participant’ attentiveness, perspective taking, and

response flexibility each mediates the effect of experimental condition on participants’

job performance. As shown in the zero-correlation matrices of the background survey,

end-of-workday survey, and patient survey in Tables 5 to 7 and the “confirmatory factor

analysis” section, attentiveness, perspective taking, and response flexibility were highly

intercorrelated and represented by a higher-order construct of patient centered behavior.

Thus, I tested the mediation hypotheses using patient centered behavior instead of its

three facets separately. I focused on patient ratings of participants’ patient centered

behavior and job performance (i.e., patient satisfaction with performance) as the

mediating mechanism and outcome, respectively. Using patient ratings instead of

participant self-reports avoided inflated relationships due to common method bias

(Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Given the complex structure of the

data, I first performed variance decomposition of variables to determine the appropriate

nesting structure. Then, I tested the mediation hypothesis following the multilevel

mediation recommendations of Preacher et al. (2010). Next, I tested the moderated

mediation hypotheses in Hypotheses 5 to 7 following the multilevel moderation

recommendations of Preacher, Zhang, & Zyphur (in press). I examined each moderator in

a separate model and then included three moderators in one model.

I performed intent-to-treat analysis using participants who attended at least one

audio during intervention (992 encounters from 41 participants and 714 patients). As a

robustness check, I replicated the above analyses using a constrained sample of

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participants who attended at least five (50%) audios during intervention (968 encounters

from 38 participants and 710 patients).

Nested Data Structure

The data structure was complex; encounter was nested within day nested with

participant while encounter was simultaneously nested within patient. This represented a

three-level model with the extra factor (i.e., patients) crossed with the level-3 units (i.e.,

participants) (Snijders & Bosker, 2012). Although extant theoretical and applied research

on cross-classified multilevel modeling has mainly focused on two-level model with a

crossed factor (e.g., Hox, 2010; Raudenbush & Bryk, 2002; for empirical examples, see

Leckie & Baird, 2011; Rasbash, Leckie, Pillinger, & Jenkins, 2010; Timmermans,

Snijders, & Bosker, 2013), statistical treatment of more complex cross-classified

multilevel models has been advanced (e.g., Goldstein, 2011; Leckie, 2013) and a limited

few applications, such as MLwiN (Rasbash, Charlton, Browne, Healy, & Cameron, 2009)

and WinBUGS (Spiegelhalter, Thomas, Best, & Lunn, 2002), have incorporated such

models using Bayesian estimation. However, a three-level model with a crossed factor

has not been implemented in Mplus (B. Muthén, June 5, 2015, personal communication).

Nonetheless, I first partitioned variance of each study variable across levels to

determine the appropriate nesting structure. As shown in the previous section (“Effect of

intervention on participant and patient outcomes”), patient satisfaction and patient rated

patient centered behavior had negligible, nonsignificant amount of variance at the day

level and significant amount of variance at the participant, patient, and encounter levels.

Thus, a cross-classified two-level model was warranted with encounter nested within the

cross-classification of participant and patient. Although I did not hypothesize patient-

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level predictors of encounter-specific outcomes, it is important to take into account the

crossed factor (i.e., patients) to obtain accurate estimates of standard errors (Meyers &

Beretvas, 2006).

Mediating Effect of Patient-Rated Patient Centered Behavior

Hypothesis 2 to 4 predicted that, compared with active control, treatment led to a

higher level of patient satisfaction (i.e., patient rated job performance) mediated by a

higher level of patient perception of patient centered behavior during encounter. The

predictor (experimental condition) was at participant level, and the mediator (patient

centered behavior) and outcome (patient satisfaction) were both at encounter level. I

tested the mediation effect following the 2-1-1 multilevel mediation framework proposed

by Preacher et al. (2010). The mediation of interest occurred at the participant level.

Focused on multilevel mediation of two-level data, Preacher et al. (2010) recommended

disentangling variables measured at level-1 in a 2-1-1 model into latent Between and

Within components to obtain unbiased estimates of Between indirect effect. This

approach is advantageous compared with the unconflated multilevel modeling approach

in which a level-1 variable is decomposed into level-2 cluster mean and level-1 variable

centered at cluster mean; treating Between component as observed cluster mean produces

biased estimates of population Between effect (Preacher et al., 2010). The multilevel

mediation approach has been adopted in recent studies (e.g., Hülsheger et al., 2013;

Zhou, Wang, Chen, & Shi, 2012) and extended to two-level data with a crossed factor

(Muthén & Muthén, 2012).

In the cross-classified two-level mediation model, estimation was simultaneously

performed at participant, patient, and within levels. At participant level, outcome latent

component was regressed on mediator latent component and observed experimental

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condition and mediator latent component was regressed on condition. Outcome and

mediator at baseline were controlled for in respective regressions. The indirect effect at

participant level was computed following Preacher et al. (2010). At patient and within

levels, outcome latent component was regressed on mediator latent component at each

level. Bayesian estimation was used for cross-classified models by default in Mplus,

which can produce unbiased estimates with a small number of clusters (Muthén, 2010).

Model fit statistics was not yet available for Bayesian analysis of cross-classified models

in Mplus 7.11. As shown in Table 36, the path from condition to patient centered

behavior ( = .06, 95% CI = [–.08, .19]) and the path from patient centered behavior to

patient satisfaction ( = .14, 95% CI = [–.17, .47]) were not significant. The indirect

effect was .004 with 95% CI of [–.02, .05] that did not exclude zero. Thus, Hypothesis 2

to 4 were not supported.

Moderating Effect of Agreeableness

Hypothesis 5 predicted that participants’ agreeableness moderated the effect of

condition (via patient perception of patient centered behavior) on patient satisfaction by

influencing the effect of condition on patient centered behavior. The moderated

mediation of interest operated at participant level as a “first stage” moderation because

the moderating effect of agreeableness occurred on the first stage of the indirect

relationship between condition and patient satisfaction (Edwards & Lambert, 2007). At

participant level of the cross-classified multilevel model, PMX was the path from X

(condition) to M (patient centered behavior); PYM was the path from M to Y (patient

satisfaction); PYX was the path from X to Y; PYM *PMX was the indirect effect; and PYX +

(PYM *PMX) was the total effect (for empirical example, see Duffy et al., 2012). Bayesian

89

estimation was used. Agreeableness was centered at participants’ mean before computing

its interaction with condition.

As shown in step 1 in Table 37, two regressions were simultaneously performed

for patient centered behavior and patient satisfaction. Condition was unrelated to patient

centered behavior ( = .06, 95% CI included zero) and patient satisfaction ( = .02, 95%

CI included zero). Agreeableness was unrelated to patient centered behavior ( = .02,

95% CI included zero) and patient satisfaction ( = .01, 95% CI included zero). In step 2,

the interaction of condition and agreeableness was significant ( = .17, 95% CI excluded

zero) but patient centered behavior was not related to patient satisfaction ( = .16, 95% CI

included zero). As shown in Table 41, the effect of condition on patient centered behavior

varied across levels of agreeableness. As shown in Figure 6, compared with active

control, treatment led to higher patient perception of patient centered behavior for

participants with high agreeableness (PMX = .19, 95% CI excluded zero) but did not differ

in patient centered behavior for participants with low agreeableness (PMX = –.11, 95% CI

included zero). The indirect effects and total effects of condition on patient satisfaction

were not significant at low (indirect effect = –.01, total effect = –.01, 95% CIs included

zero) and high (indirect effect = .03, total effect = .04, 95% CIs included zero) levels of

agreeableness. Thus, Hypothesis 5 was not supported.

Moderating Effect of Perceived Workgroup Patient Care Climate

Hypothesis 6 predicted that participants’ perception of workgroup patient care

climate moderated the effect of condition (via patient perception of patient centered

behavior) on patient satisfaction by influencing the effect of condition on patient centered

behavior. As in the moderating hypothesis of agreeableness, the moderated mediation of

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interest operated at participant level as a “first stage” moderation (Edwards & Lambert,

2007). Bayesian estimation was used. Perceived patient care climate was centered at

participants’ mean before computing its interaction with condition.

As shown in step 1 in Table 38, two regressions were simultaneously performed

for patient centered behavior and patient satisfaction; perceived patient care climate was

unrelated to patient centered behavior ( = .04, 95% CI included zero) and patient

satisfaction ( = .02, 95% CI included zero). In step 2, the interaction of condition and

perceived patient care climate was not significant ( = .001, 95% CI included zero) and

patient centered behavior was not related to patient satisfaction ( = .13, 95% CI included

zero). As shown in Table 41, the effect of condition on patient centered behavior did not

vary across levels of perceived patient care climate. The indirect effects and total effects

of condition on patient satisfaction were not significant at low (indirect effect = .004,

total effect = .02, 95% CIs included zero) and high (indirect effect = .001, total effect

= .01, 95% CIs included zero) levels of perceived patient care climate. Thus, Hypothesis

6 was not supported.

Moderating Effect of Perceived Daily Work Overload

Hypothesis 7 predicted that participants’ daily work overload moderated the effect

of condition (via patient perception of patient centered behavior) on patient satisfaction

by influencing the effect of condition on patient centered behavior. Daily work overload

had significant amounts of variance at the day and participant levels (participant-level

variance = 0.56, p < .001; day-level variance = 0.66, p < .001). Following the multilevel

moderation framework recommended by Preacher et al. (in press), the moderating effect

of work overload (which was measured at level-1) on the effect of level-2 condition on

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level-1 patient centered behavior operated at level-2 (i.e., participant level). Thus, the

moderated mediation need be tested at participant level using observed predictor

(condition) and, ideally, participant level latent components of mediator (patient centered

behavior), moderator (work overload), and outcome (patient satisfaction). Given that

latent variable interaction (of latent work overload and observed condition) has not been

implemented in Bayesian estimation of cross-classified model in Mplus (L. Muthén, June

05, 2015, personal communication) (but can be handled in WinBUGS; Marsh, Wen, Hau,

& Nagengast, 2013), I used participants’ means of daily work overload across 10 days

during intervention as an observed participant level moderator. The unconflated approach

has been widely accepted (e.g., MacKinnon, 2008; Zhang, Zyphur, & Preacher, 2009),

although it may be bias-prone compared with the latent decomposition approach

(Preacher et al., 2010). Bayesian estimation was used in the cross-classified model.

Participant level mean work overload was grand-mean centered before computing its

interaction with condition.

As shown in step 1 in Table 39, two regressions were simultaneously performed

for patient centered behavior and patient satisfaction; mean work overload was unrelated

to patient centered behavior ( = –.02, 95% CI included zero) and patient satisfaction ( =

–.03, 95% CI included zero). In step 2, the interaction of condition and mean work

overload was not significant ( = –.09, 95% CI included zero) and patient centered

behavior was not related to patient satisfaction ( = .18, 95% CI included zero). As shown

in Table 41, the effect of condition on patient centered behavior did not vary across levels

of mean work overload. The indirect effects and total effects of condition on patient

satisfaction were not significant at low (indirect effect = .02, total effect = .03, 95% CIs

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included zero) and high (indirect effect = –.001, total effect = .003, 95% CIs included

zero) levels of mean work overload. Thus, Hypothesis 7 was not supported.

Supplemental Analyses

I tested the three moderators simultaneously by including them and their

respective interaction terms with condition in the same model. As shown in Table 40,

same pattern of results were obtained. In step 2, the interaction of condition and

agreeableness was a significant predictor of patient centered behavior ( = .18, 90% CI

excluded zero). In Table 41, the effect of condition on patient centered behavior varied

across levels of agreeableness. Compared with active control, treatment led to higher

patient perception of patient centered behavior for participants with high agreeableness

(PMX = .20, 90% CI excluded zero) but did not differ in patient centered behavior for

participants with low agreeableness (PMX = –.12, 95% CI included zero). The indirect

effects and total effects were not different across levels of agreeableness.

I replicated the above analyses using a restrained sample of participants who

attended at least five audios during intervention (968 encounters from 38 participants and

710 patients). The same pattern of results were obtained.

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Chapter 7 Discussion

Summary of Study Findings

This dissertation examined the role of a brief mindfulness intervention in

promoting job performance in service settings and the client-focused mediating

mechanisms (i.e., attentiveness, perspective taking, and response flexibility) and

individual, social, and job contingencies (i.e., agreeableness, perceived workgroup

service climate, and daily work overload) of this relationship. I conducted a pretest-

posttest field experiment with random assignment to a treatment and active control

condition in a health care organization. A series of confirmatory factor analyses of the

client-focused items in the background, end-of-workday, and patient surveys converged

to suggest that attentiveness, perspective taking, and response flexibility were represented

by a higher-order construct of patient centered behavior. Using multilevel modeling and

latent growth modeling, in comparisons of the treatment (i.e., mindfulness meditation)

and active control (i.e., wellness education) conditions, treatment yielded pre-to-post

increases in mindfulness and work related behaviors in terms of self-reported job

performance and proactive patient care and patient ratings of patient centered behavior.

Multilevel moderated mediation modeling with cross classification suggests that

compared with active control, treatment produced a higher level of the proposed

mediator, patient-rated patient centered behavior, for participants higher in agreeableness.

Theoretical and Empirical Implications

Past research into the role of mindfulness and mindfulness-based practices in job

performance has yielded mixed findings. This research has focused on performance

across a variety of settings, such as restaurant (Dane & Brummel, 2014), health care

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(Grepmair et al., 2007), power plant (Zhang et al., 2013), MBA classes (Shao &

Skarlicki, 2009), university employees (Giluk, 2010), insurance company (Wolever et al.,

2012), and heterogamous organizations (Reb et al., 2015). The nature of task

environment may be key to channeling mindful awareness and attention toward fulfilling

task-related duties. Dane (2011) has suggested that heightened attention toward external

stimuli is conducive to task performance in dynamic task environments such as service

settings (see also Glomb et al., 2011). By focusing on the role of mindfulness in job

performance in service settings, this dissertation offers a more clear delineation of the

external validity (i.e., generalizability) of the performance effect of mindfulness and

information into the intervening processes.

Given that little is known regarding the mechanisms by which mindfulness and

mindfulness-based practices effect performance, this work contributes to knowledge of

the client-focused behavioral mechanisms—attentiveness, perspective taking, and

response flexibility—that are germane to service settings in general and health care

settings in particular. The emergence of the higher-order construct of patient centered

behavior in participants’ self-reports echoes the theoretical argument that the three

mechanisms are driven by intertwined core processes of mindfulness, including

decoupling of the self from events and experiences, decreased automaticity, and

physiological regulation (Glomb et al., 2011). The emergence of the higher-order

construct in patient ratings suggests that the three behavioral constructs manifest in a

coherent fashion and that observers tend to develop global rather than faceted perceptions

of participants’ behaviors during encounters.

95

The mindfulness intervention, compared with active control, did not yield

consistently higher patient ratings across outcomes. Specifically, it only yielded effects

for one of the key job performance outcome—patient centered behaviors (both self and

patient rated)—but not for patient satisfaction. The absence of strong and consistent

effects of the intervention on outcomes may be related to several design factors.

Unlike prior research often comparing mindfulness intervention with a nonactive

control condition in which participants were waitlisted or worked as usual (e.g.., Aikens

et al., 2014; Grepmair et al., 2007; Hülsheger et al., 2013; Wolever et al., 2012), I used an

active control condition in which participants listened to wellness education audios.

Given that the active control and treatment conditions share all intervention elements

(i.e., daily 10-minute audios, daily end-of-workday surveys, and patient survey handoffs)

except the contents of audios, one would have more confidence in attributing differences

in an outcome across conditions to the contents of audios (Cook & Campbell, 1979;

Goyal et al., 2014). However, it is also more challenging to detect such differences when

the treatment condition was compared with an active control condition than with a

nonactive control condition for two important reasons. First, participants in the active

control condition are likely to expect that they will have improvements as a result of

engaging in some kinds of intervention activity and be motivated to behave equally well

as their counterparts in the treatment condition (Shadish et al., 2002). Second, because

my treatment and active control conditions both use audios pertinent to some aspects of

well-being, one may question whether the active control should really be considered a

control, but rather an alternative type of intervention. Taken together, the multilevel

modeling and latent growth modeling analytical approaches reveal that, compared with

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active control, treatment yielded pre-to-post increases in self-reported mindfulness,

proactive patient care, job performance, and self- and patient-reported patient centered

behavior, and decrease in fatigue. One could suggest that the effect of mindfulness

meditations are actually greater, but since the wellness education contents may have also

improved outcomes, these results seem more modest.

There were also several factors that were not related to the design specifically, but

rather how the study played out. Specifically, participants in the treatment and active

control conditions had high levels of baseline patient satisfaction (i.e., an average of 4.86

out of 5.00 in each condition) and small amounts of variance, which creates a ceiling

effect that makes pre-to-post improvements difficult to achieve. Further, I have a small

number of participants in the analysis, rendering low power to detect significant effects.

The absence of intervention effect may also be attributed to the degree of

compliance. As Shadish et al., (2002) noted, “failure to get the full intervention…[is]

common in field experimentation” (p. 315). Compliance to intervention has been a

concern in past research involving daily meditation among working adults (e.g.,

Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008; Hülsheger et al., 2013). Despite my

effort to encourage adherence, including setting up designated rooms in the workplace for

audio listening, extending the morning sessions to accommodate participants’ start times,

and offering makeup sessions during the lunch break, arriving at the workplace 15

minutes earlier in the morning for the audio sessions appears to be challenging for some

participants and on some days. In the debrief survey, I probed why participants may have

missed an audio session. The common reasons for missing a session include sick leave,

attending to family duties, and change of work schedule.

97

Across the treatment and active control conditions, participants attended an

average of 6.79 out of 10 audios, ranging from 0 to 10. Thus, the (null) intervention effect

using condition membership as the predictor falls short in capturing the effect of truly

receiving the treatment and active control interventions. Although I did not find

significant intervention effect on patient satisfaction for participants attending at least one

or at least five audios, compared with active control, treatment produced higher self-

reported proactive patient care and patient-rated patient centered behavior for participants

attending a higher (1 SD above the mean) rather than lower (1 SD below the mean)

number of audios. This suggests the possibility that the intervention effect on patient

satisfaction may exist among participants with almost perfect compliance (e.g., attending

at least eight audios).

Lastly, the absence of intervention effect on patient satisfaction could be due to

diffusion, a threat to internal validity in which participants in one condition know the

contents of intervention in the other condition; however, in this case I believe it is

unlikely. During enrollment, participants indicated agreements not to discuss audio

content with coworkers, supervisors, and patients during study period. Further, because

the audios rotated in both conditions, participants did not all hear the same order and so

may have been less likely to discuss as they did not have common experiences each day.

In the debrief survey, I probed the extent to which participants were aware of the audio

contents of their coworkers and the responses were on the low end.

Mediation and moderation. Patient rated patient centered behavior failed to

mediate the effect of experimental condition on patient satisfaction due to nonsignificant

predictor mediator path and mediator outcome path. The null finding is likely

98

attributed to a constellation of factors discussed above, including high baselines of the

mediator and outcome, small amounts of variance, limited compliance, and a small

sample size. Nonetheless, compared with active control, a mindfulness intervention

fosters higher patient-rated patient centered behavior for participants attending a high

(rather than low) number of audios, highlighting compliance as a key factor for the

mindfulness intervention to be efficacious. The effect size, although small, is impressive

considering the small time investment in intervention (10 minutes each day for 10 days

vs. 8 weeks as in MBSR) and objective ratings of outcome from patients who are blind to

the interventions (Prentice & Miller, 1992).

Although participants’ agreeableness failed to moderate the mediated effect of

experimental condition (through patient rated patient centered behavior) on patient

satisfaction, agreeableness affected the effect of experimental condition on patient rated

patient centered behavior such that, compared with active control, treatment led to higher

patient rated patient centered behavior for participants higher in agreeableness. This

finding confirms the aptitude-treatment interaction argument (Snow, 1991) that

mindfulness intervention is more effective for participants with certain attributes such as

a high level of agreeableness.

Participants’ perceived workgroup patient care climate failed to moderate the

mediated effect of experimental condition (through patient rated patient centered

behavior) on patient satisfaction. According to the norm activation theory (Schwartz,

1977), it was proposed that the mindfulness intervention makes participants who perceive

strong patient care climate develop heightened awareness that quality patient care is

expected, supported, and rewarded, internalize situational demands into their personal

99

goals, and feel obligated that they should provide quality service. However, the

mindfulness intervention may also make participants behave more discretionarily in the

ways they fulfill obligations. For example, instead of showing improvements in the

interpersonal aspects (i.e., attending to, understanding, and reacting flexibility to

patients), participants may choose to provide quality health care by making more accurate

diagnoses, working more efficiently and quickly. Thus, the mindfulness intervention,

coupled with higher perceptions of patient care climate, may not necessarily lead to

patient centered behaviors.

Participants’ perceived work overload failed to moderate the mediated effect of

experimental condition (through patient rated patient centered behavior) on patient

satisfaction. This is likely due to the small number of participants in the analyses. As

McClelland and Judd (1993) suggests, it is difficult to detect significant interaction

between a categorical predictor and a moderator when the sample size is small.

This dissertation also makes methodological contributions to organizational

research in mindfulness. Instead of adopting a constellation of activities (e.g., raisin

eating, sitting meditation, body scan, loving-kindness meditation) of the popular

Mindfulness-Based Stress Reduction (MBSR) program, this dissertation focuses on one

activity (i.e., sitting meditation) from the MBSR to detect its effects on outcomes. To

make stronger causal inferences, I use a field experiment with the treatment and active

control conditions, pretest-posttest repeated measures over 15 days, and participant- and

patient-ratings of outcomes. Random assignment of participants into conditions rules out

other covariates as the predictors of outcome (Shadish et al., 2002). Using an active

control group allows stronger confidence in attributing differences in an outcome to the

100

contents of the intervention rather than the process of implementing the intervention

(Cook & Campbell, 1979; Goyal et al., 2014). Compared with participants’ self-reports,

patient ratings of outcomes allow for objective measures that are resistant to common

method bias and experimental demands effect (Podsakoff et al., 2003; Shadish et al.,

2002).

Practical Implications

A growing number of organizations, such as Google, Apple, and General Mills,

have begun to incorporate mindfulness-based practices into their workplace wellness

programs (Tetrik & Winslow, 2015). A brief yet efficacious mindfulness intervention at

work would be welcomed by employees with busy work lives. This dissertation offers

such an intervention. The mindfulness meditation is brief, efficacious, easily accessible,

and cost effective to organizations. As my findings suggest, for certain employees, a daily

time investment of 10 minutes yields higher client ratings of client-focused behaviors

(i.e., paying attention to clients, taking clients’ perspectives, and reacting flexibly to

clients), higher self-reports of job performance and mindfulness, and lower fatigue.

Organizations should incorporate mindfulness practices that allow flexibility for

employees to engage in. In my study, meditating for 10 minutes in the morning at work

for 10 days appeared to be challenging for some participants and on some days. An

accessible mindfulness practice needs to be tailored around employees’ work schedules

and activities. For example, employees could bring mindful awareness and attention

while having lunch, taking a break, or even performing routine activities like walking

down a hallway or washing their hands. Employees may also have the option to meditate

in a designated room as a group or individually in their own offices.

101

Organizations need to be aware that a mindfulness intervention may be more (or

less) effective for employees with certain characteristics and in certain social contexts at

work. As my findings suggest, the mindfulness intervention is more effective for

participants higher in agreeableness. Workplace norms, culture, and job characteristics

may influence the extent to which a mindfulness intervention translates into work related

outcomes. For example, service jobs focusing on people may utilize different mindful

skills than manufacturing jobs focusing on equipment (Dane, 2011).

Limitations and Future Directions

This dissertation is not without limitations. First, my aim was to examine the

effect of a brief mindfulness meditation at work. However, one may question whether 10

minutes of meditation per day for 10 days were sufficient for the intervention to make a

change to participants (Carmody & Baer, 2009). Past field interventions have generally

required more intensive mindfulness practices, such as one hour class each week plus a 2-

hour retreat over 12 weeks (Wolever et al., 2012), one hour each morning over 9 weeks

(Grepmair et al., 2007), and 2.5 hours per week plus an all-day retreat over 8 weeks (e.g.,

Giluk, 2010). My findings suggest that the mindfulness intervention has an effect on

certain outcomes (i.e., patient rated patient centered behavior and self rated proactive

patient care) only for participants attending a high number of audios, indicating that the

brief mindfulness training may have been even more influential in effecting changes if it

had persisted over a longer period of time.

Second, the patient rated outcomes have high baselines, yielding ceiling effect

that makes it difficult for the mindfulness intervention to make improvements. Despite

these high ratings, the mindfulness intervention produced higher patient rated patient

102

centered behavior, making these findings more impressive. Future research might adopt a

pre-screening procedure to enroll participants who have a low level of outcome to allow

observable changes resulting from a mindfulness intervention (e.g., Wolever et al., 2012).

Third, despite random assignment of participants into conditions, attrition of

participants could jeopardize randomization. In comparing the baseline outcomes

between the treatment and active control conditions for participants retained in the

analyses, results suggests that attrition is not a concern for randomization.

Fourth, although participants were asked to handoff a survey to every patient they

assisted each day, for some participants, the number of patient surveys returned on a

given day tend to be fewer than the total number of patients they assisted that day as

reported in the end-of-workday survey. Patient ratings obtained were unlikely to cover all

the encounters on all days for all participants, introducing measurement errors in the

aggregated measures of patient rated outcomes. In the debrief survey, participants

indicated the common reasons for missing a survey include returning patients who had

taken a survey before, too busy working, and patient declining the survey. To the extent

that patients who are unsatisfied with the encounter may have declined the survey, the

effect of the mindfulness intervention may be overestimated. I included obtained patient

ratings of all encounters in the analyses. However, some encounters were very brief (e.g.,

less than one minute), which may introduce biases in patients’ perceptions of

participants’ behaviors.

Fifth, I focus on cognitive empathy (i.e., perspective taking) as one of the client-

focused behavioral constructs. However, a mindfulness meditation may also foster the

affective aspect of empathy, or emotional concern (Scott & Duffy, 2015). A mindfulness

103

intervention may make individuals see the world through others’ eyes and feel others’

emotions and sufferings, leading to a desire to care and concern for others. Future

research could examine the role of a mindfulness intervention in promoting affective

empathy in conjunction with cognitive empathy.

Sixth, my sample size is small, rendering low power to detect significant effects.

Future research could replicate the field experiment in other organizations to obtain a

larger sample to evaluate the effect of the mindfulness intervention with greater power.

Conclusion

This dissertation examines the role of a micro mindfulness intervention in

promoting job performance in service settings and its client-focused mechanisms and

individual, social, and job contingencies. In the field experiment in a health care

organization, a mindfulness intervention, compared with active control, leads to higher

self-reported proactive patient care and patient ratings of patient centered behavior

among health care professionals who attended a higher number of audios. Compared with

active control, the mindfulness intervention produces day-to-day increases in self-

reported mindfulness, job performance, and patient centered behavior and decline in

fatigue over time. The mindfulness intervention also yields higher patient rated patient

centered behaviors for health care professionals who have a higher level of

agreeableness. The findings suggest that a brief mindfulness intervention may be

beneficial for employees’ job performance in interpersonal settings.

104

Tabl

e 1

Sum

mar

y of

Stu

dies

Lin

king

Min

dful

ness

to J

ob P

erfo

rman

ceSt

udy

Sam

ple

and

desi

gnM

indf

ulne

ssJo

b pe

rfor

man

ceC

ontin

genc

yM

echa

nism

Res

ults

Corr

elat

iona

l Stu

dies

Dan

e &

B

rum

mel

, 201

4

Cro

ss se

ctio

nal s

urve

y of

re

stau

rant

serv

ers i

n th

e U

nite

d St

ates

Empl

oyee

self-

repo

rted

trait

wor

kpla

ce

min

dful

ness

mea

sure

d by

th

e ad

apte

d M

indf

ul

Aw

aren

ess A

ttent

ion

Scal

e

A c

ompo

site

of

man

ager

-rat

ed si

ngle

-ite

m o

vera

ll jo

b pe

rfor

man

ce a

nd th

e le

vel o

f bus

ynes

s of

the

rest

aura

nt se

ctio

n a

serv

er is

ass

igne

d to

.

Not

exa

min

edN

ot

exam

ined

Wor

kpla

ce m

indf

ulne

ss

is p

ositi

vely

cor

rela

ted

with

job

perf

orm

ance

(r=

.23)

, eve

n af

ter

cont

rolli

ng fo

r thr

ee fa

cets

of

wor

k en

gage

men

t (i.e

., vi

gor,

dedi

catio

n, a

nd

abso

rptio

n).

Zhan

g et

al

., 20

13C

ross

sect

iona

l sur

vey

of

nucl

ear p

ower

pla

nt

oper

ator

s in

Chi

na

Empl

oyee

self-

repo

rted

trait

min

dful

ness

(i.e

., pr

esen

ce a

nd a

ccep

tanc

e fa

cets

) mea

sure

d by

the

Frei

burg

Min

dful

ness

In

vent

ory

Supe

rvis

or ra

ted

task

pe

rfor

man

ce m

easu

red

by W

illia

ms a

nd

And

erso

n’s (

1991

) sc

ale

Task

co

mpl

exity

Not

ex

amin

edPr

esen

ce fa

cet i

s un

rela

ted

to ta

sk

perf

orm

ance

(r=

.08)

. Pr

esen

ce fa

cet i

s po

sitiv

ely

corr

elat

ed w

ith

task

per

form

ance

for

empl

oyee

s in

high

task

co

mpl

exity

jobs

and

is

nega

tivel

y co

rrel

ated

with

ta

sk p

erfo

rman

ce fo

r em

ploy

ees i

n lo

w ta

sk

com

plex

ity jo

bs.

Acc

epta

nce

face

t is

unre

late

d to

task

pe

rfor

man

ce (r

= .0

7),

rega

rdle

ss o

f tas

k co

mpl

exity

.R

eb e

t al

., 20

15Tw

o-w

ave

surv

ey o

f w

orki

ng a

dults

in a

var

iety

of

org

aniz

atio

ns in

Si

ngap

ore.

Empl

oyee

self-

repo

rted

trait

wor

kpla

ce

min

dful

ness

(i.e

., aw

aren

ess a

twor

k)

mea

sure

d by

the

adap

ted

Five

Fac

tor M

indf

ulne

ss

Que

stio

nnai

re

Tim

e-la

gged

su

perv

isor

-rat

ed ta

sk

perf

orm

ance

mea

sure

d by

Mot

owid

lo a

nd

Scot

ter’

s (19

94) s

cale

Not

exa

min

edN

ot

exam

ined

Wor

kpla

ce m

indf

ulne

ss

is p

ositi

vely

rela

ted

to ta

sk

perf

orm

ance

(r=

.20)

.

105

Shao

&

Skar

lick

i, 20

09

Cro

ss se

ctio

nal s

urve

y of

m

anag

ers a

ttend

ing

an

MB

A p

rogr

am in

Can

ada

Man

ager

s sel

f-re

porte

d tra

it m

indf

ulne

ss m

easu

red

by th

e M

indf

ul A

war

enes

s A

ttent

ion

Scal

e

Aca

dem

ic

perf

orm

ance

as a

co

mpo

site

of g

rade

s of

a va

riety

of c

ours

es,

proj

ect w

orks

, and

cl

ass p

artic

ipat

ion.

Gen

der

Not

ex

amin

edTr

ait m

indf

ulne

ss is

po

sitiv

ely

rela

ted

to

acad

emic

per

form

ance

on

ly fo

r wom

en.

Gilu

k,

2010

Long

itudi

nal s

urve

y of

w

orki

ng a

dults

acr

oss

expe

rimen

tal(

8-w

eek

Min

dful

ness

-Bas

ed S

tress

R

educ

tion

or M

indf

ulne

ss-

Bas

ed C

ogni

tive

Ther

apy

train

ing)

and

cont

rol

grou

ps(n

o tra

inin

g)

Empl

oyee

s sel

f-re

porte

d tra

it m

indf

ulne

ss m

easu

red

by th

e Fi

ve F

acto

r M

indf

ulne

ss Q

uest

ionn

aire

at

T1,

T2

(8 w

eeks

late

r),

and

T3(4

wee

ks a

fter T

2).

Supe

rvis

or ra

tings

of

wor

k pe

rfor

man

ce

(i.e.

, a c

ompo

site

of

task

per

form

ance

, co

mm

unic

atio

n,

team

wor

k, c

hang

e)

usin

g an

ada

pted

m

easu

re fr

om C

olbe

rt et

al.

(200

8) a

t T1,

T2,

an

d T3

.

Not

exa

min

edPo

sitiv

e an

d ne

gativ

e af

fect

; R

elat

ions

hip

qual

ity w

ith

cow

orke

rs

Cro

ss se

ctio

nal

corr

elat

ions

bet

wee

n tra

it m

indf

ulne

ss a

nd w

ork

perf

orm

ance

at T

1, T

2,

and

T3 a

re a

ll no

nsig

nific

ant (

r=–.

22, .

06, .

01).

Trai

t min

dful

ness

at T

1 is

unr

elat

ed to

wor

k pe

rfor

man

ce a

t T2

and

T3

(r=

.03,

–.2

6).

Aff

ect a

nd re

latio

nshi

p qu

ality

fail

to m

edia

te th

e re

latio

nshi

p be

twee

n m

indf

ulne

ss a

nd w

ork

perf

orm

ance

.

Bea

ch e

t al

., 20

13C

ross

-sec

tiona

l sur

vey

of

clin

icia

ns c

arin

g fo

r pa

tient

s with

hum

an

imm

unod

efic

ienc

y vi

rus

Clin

icia

ns se

lf-re

porte

d tra

it m

indf

ulne

ss m

easu

red

by M

indf

ul A

ttent

ion

Aw

aren

ess S

cale

.

Clin

icia

ns’

com

mun

icat

ion

qual

ity

code

d fr

om

reco

rdin

gs; p

atie

nt

ratin

gs o

f clin

icia

ns’

com

mun

icat

ion

and

patie

nt o

vera

ll sa

tisfa

ctio

n

Not

exa

min

edN

ot

exam

ined

Com

pare

d w

ith

clin

icia

ns w

ith th

e lo

wes

t te

rtile

min

dful

ness

scor

es,

clin

icia

ns w

ith th

e hi

ghes

t te

rtile

min

dful

ness

scor

es

had

high

er p

atie

nt-

cent

ered

com

mun

icat

ion

(i.e.

, rap

port

build

ing

and

disc

ussi

on fo

psy

chos

ocia

l is

sues

) and

hig

her p

atie

nt

over

all s

atis

fact

ion.

106

Inte

rven

tion

Stud

ies

Shon

in

et a

l.,

2014

Fiel

d ex

perim

ent i

n w

hich

of

fice-

base

d m

iddl

e m

anag

ers f

rom

he

tero

gene

ous

orga

niza

tions

wer

e ra

ndom

ly a

ssig

ned

into

8-

wee

k M

edita

tion

Aw

aren

ess T

rain

ing

(MA

T)or

an

activ

e co

ntro

l co

nditi

on in

volv

ing

educ

atio

n of

cog

nitiv

e-be

havi

oral

prin

cipl

e.

Med

itatio

n A

war

enes

s Tr

aini

ng (M

AT)

is a

n 8-

wee

k pr

ogra

m in

volv

ing

90-m

inut

e w

orks

hop

each

w

eek,

CD

-gui

ded

daily

se

lf-pr

actic

e, a

nd a

one

-to-

one

50-m

inut

e su

ppor

t se

ssio

n w

ith th

e fa

cilit

ator

on

a fo

ur-w

eekl

y ba

sis.

MA

T in

clud

ed m

edita

tion

tech

niqu

es to

fost

er

min

dful

ness

, citi

zens

hip,

pe

rcep

tive

clar

ity, e

thic

al

and

com

pass

iona

te

awar

enes

s pat

ienc

e,

gene

rosi

ty, a

nd

pers

pect

ive.

Supe

rvis

or ra

tings

of

role

-bas

ed

perf

orm

ance

(i.e

., a

com

posi

te o

f wor

k ro

les i

nvol

ving

job,

ca

reer

, inn

ovat

or, t

eam

m

embe

r, an

d or

gani

zatio

nal c

itize

n)

usin

g th

e m

easu

re

from

Wel

bour

ne,

John

son,

& E

rez

(199

8) a

sses

sed

at

base

line,

con

clus

ion

of

inte

rven

tion,

and

th

ree-

mon

th fo

llow

-up

.

Not

exa

min

edN

ot

exam

ined

Com

pare

d w

ith a

ctiv

e co

ntro

l, M

edita

tion

Aw

aren

ess T

rain

ing

led

to

high

er im

prov

emen

ts o

f jo

b pe

rfor

man

ce a

t the

co

nclu

sion

of i

nter

vent

ion

and

thre

e-m

onth

follo

w-

up.

Gilu

k,

2010

Fiel

d qu

asi-e

xper

imen

t in

whi

ch w

orki

ng a

dults

in

the

U.S

. sel

f-se

lect

into

ex

perim

enta

l gro

up th

at

rece

ives

8-w

eek

Min

dful

ness

-Bas

ed S

tress

R

educ

tion

or M

indf

ulne

ss-

Bas

ed C

ogni

tive

Ther

apy

train

ing

or c

ontro

l gro

up

that

rece

ives

nei

ther

tra

inin

g

Expe

rimen

tal g

roup

in

volv

es 8

-wee

k M

indf

ulne

ss-B

ased

Stre

ss

Red

uctio

n or

Min

dful

ness

-B

ased

Cog

nitiv

e Th

erap

y.

Supe

rvis

or ra

tings

of

gene

ral w

ork

perf

orm

ance

(i.e

., a

com

posi

te o

f tas

k pe

rfor

man

ce,

com

mun

icat

ion,

te

amw

ork,

cha

nge)

m

easu

red

by a

n ad

apte

d sc

ale

from

C

olbe

rt et

al.

(200

8) a

t pr

e-tra

inin

g, p

ost-

train

ing,

and

4-w

eek

follo

w-u

p.

Not

exa

min

edN

ot

exam

ined

Expe

rimen

tal g

roup

sh

ows h

ighe

r wor

k pe

rfor

man

ce th

an d

oes

cont

rol g

roup

at 4

-wee

k fo

llow

-up,

but

no

mea

n di

ffere

nces

in w

ork

perf

orm

ance

are

obs

erve

d be

twee

n th

e tw

o gr

oups

at

post

-trai

ning

.

107

Gre

pmai

r et a

l.,

2007

Psyc

hoth

erap

ists

-in-

train

ing

in G

erm

any

rece

ived

9-w

eek

regu

lar

train

ing

(pre

) fol

low

ed b

y 9-

wee

k Ze

n m

edita

tion

(pos

t).

Zen

med

itatio

n w

as o

ne

hour

eac

h m

orni

ng fo

r nin

e w

eeks

, inv

olvi

ng

mot

ionl

ess s

ittin

g an

d fo

cuse

d at

tent

ion

on

brea

thin

g.

Ther

apeu

tic

perf

orm

ance

mea

sure

d as

redu

ctio

n of

ph

ysic

al a

nd

psyc

holo

gica

l sy

mpt

oms o

f(d

iffer

ent)

patie

nts

treat

ed d

urin

g pr

e-an

d po

st-p

erio

ds.

Not

exa

min

edN

ot

exam

ined

Com

pare

d w

ith re

gula

r tra

inin

g pe

riod,

ps

ycho

ther

apis

ts-in

-tra

inin

gtra

nsiti

oned

into

med

itatio

n pe

riod

had

bette

r the

rape

utic

pe

rfor

man

ce in

dica

ted

by

grea

ter r

educ

tion

of p

atie

nt

sym

ptom

s.

108

Tabl

e 2

Stan

dard

ized

Item

Loa

ding

s in

the

Hig

her-

Ord

er-F

acto

r Mod

el in

the

Part

icip

ant B

ackg

roun

d Su

rvey

Item

A

ttent

iven

ess

Pers

pect

ive

taki

ngR

espo

nse

flexi

bilit

y

I lis

ten

care

fully

to p

atie

nts.

0.88

I car

eful

ly o

bser

ve h

ow p

atie

nts r

espo

nd.

0.87

I ask

que

stio

ns th

at sh

ow m

y un

ders

tand

ing

of p

atie

nts’

situ

atio

ns.

0.82

I sho

w p

atie

nts t

hat I

am

list

enin

g by

usi

ng v

erba

l ack

now

ledg

emen

ts o

r bod

y la

ngua

ge (e

.g.,

head

nod

s).

0.81

I am

sens

itive

to p

atie

nts’

subt

le m

eani

ngs o

r cue

s.0.

75I f

requ

ently

try

to ta

ke p

atie

nts’

per

spec

tives

. 0.

88I r

egul

arly

seek

to u

nder

stan

d pa

tient

s’ v

iew

poin

ts.

0.78

I mak

e an

eff

ort t

o se

e th

e w

orld

thro

ugh

patie

nts’

eye

s. 0.

75I o

ften

imag

ine

how

pat

ient

s are

feel

ing.

0.

73I r

espo

nd to

pat

ient

s afte

r car

eful

con

side

ratio

n.

0.83

I thi

nk o

f mor

e th

an o

ne w

ay to

reac

t to

patie

nts.

0.73

I pau

se to

con

side

r mul

tiple

opt

ions

bef

ore

resp

ondi

ng to

pat

ient

s. 0.

72I r

eact

to p

atie

ntsb

ased

on

thei

r situ

atio

ns.

0.63

Not

e.N

= 72

par

ticip

ants

.

All

item

load

ings

had

ps<

.001

.

109

Tabl

e 3

Stan

dard

ized

Item

Loa

ding

s fro

m th

e M

ultil

evel

Con

firm

ator

y Fa

ctor

Ana

lysi

s of t

he H

ighe

r-O

rder

-Fac

tor M

odel

of P

atie

nt

Inte

ract

ion

Item

s in

the

Part

icip

ant E

nd-o

f-Wor

kday

Sur

vey

Item

W

ithin

Bet

wee

nAt

tent

iven

ess

Toda

y at

wor

k, I

liste

ned

care

fully

to p

atie

nts.

0.75

0.96

Toda

y at

wor

k, I

was

sens

itive

to p

atie

nts’

subt

le m

eani

ngs o

r cue

s.0.

710.

98Pe

rspe

ctiv

e ta

king

Toda

y at

wor

k, I

tried

to ta

ke p

atie

nts’

per

spec

tives

.0.

821.

00To

day

at w

ork,

I m

ade

an e

ffor

t to

see

the

wor

ld th

roug

h pa

tient

s’ e

yes.

0.81

0.99

Resp

onse

flex

ibili

tyTo

day

at w

ork,

I pa

used

to c

onsi

der m

ultip

le o

ptio

ns b

efor

e re

spon

ding

to p

atie

nts.

0.64

1.00

Toda

y at

wor

k, I

thou

ght o

f mor

e th

an o

ne w

ay to

reac

t to

patie

nts.

0.59

0.98

Not

e.N

= 78

2 da

ily o

bser

vatio

ns n

este

d w

ithin

69

parti

cipa

nts.

Dai

ly o

bser

vatio

ns w

ere

incl

uded

if th

ey w

ere

resp

onde

d to

on

the

sam

e da

y a

surv

ey w

as se

nt o

r by

noon

the

follo

win

g da

y.

With

in-le

vel i

tem

load

ings

repr

esen

ted

the

with

in-p

artic

ipan

t fac

tor s

truct

ure

deriv

ed fr

om c

ovar

ianc

e m

atrix

of i

tem

scor

es c

ente

red

at e

ach

parti

cipa

nt’s

mea

n. B

etw

een-

leve

l ite

m lo

adin

gs re

pres

ente

d be

twee

n-pa

rtici

pant

fact

or st

ruct

ure

deriv

ed fr

om c

ovar

ianc

e

mat

rix o

f par

ticip

ants

’ mea

n ite

m sc

ores

(Dye

r et a

l., 2

005;

Mut

hén,

1994

).

110

Tabl

e 4

Stan

dard

ized

Item

Loa

ding

s fro

m th

e C

ross

-Cla

ssifi

ed C

onfir

mat

ory

Fact

or A

naly

sis o

f the

Hig

her-

Ord

er-F

acto

r Mod

el o

f Pat

ient

In

tera

ctio

n Ite

ms i

n th

e Pa

tient

Sur

vey

Item

W

ithin

-leve

lPa

rtici

pant

-leve

lPa

tient

-leve

lAt

tent

iven

ess

This

per

son

was

sens

itive

to m

ysu

btle

mea

ning

s or c

ues.

0.88

0.96

1.00

This

per

son

care

fully

obs

erve

d ho

w I

resp

onde

d.

0.76

0.97

0.98

This

per

son

liste

ned

care

fully

to m

e.0.

700.

840.

93Pe

rspe

ctiv

e ta

king

This

per

son

tried

to ta

ke m

y pe

rspe

ctiv

e.0.

840.

960.

99Th

is p

erso

n m

ade

an e

ffor

t to

see

the

wor

ld th

roug

h m

yey

es.

0.81

0.97

0.98

This

per

son

soug

ht to

und

erst

and

my

view

poin

t.0.

790.

980.

99Re

spon

se fl

exib

ility

This

per

son

thou

ght o

f mor

e th

an o

ne w

ay to

reac

t to

me.

0.85

0.84

0.94

This

per

son

resp

onde

d to

me

afte

r car

eful

con

side

ratio

n.

0.76

0.97

0.98

This

per

son

paus

ed to

con

side

r mul

tiple

opt

ions

bef

ore

resp

ondi

ng to

me.

0.73

0.97

0.97

Not

e.N

= 1,

952

enco

unte

rs n

este

d w

ithin

53

parti

cipa

nts a

nd 1

,357

pat

ient

s.

All

Bay

esia

n 95

% c

redi

bilit

y in

terv

als e

xclu

ded

zero

.

111

Table 5Mean, Standard Deviation, Cronbach’s , and Zero-Order Correlations of Study Variables in the Participant Background Survey

M SD 1 2 3 41 Condition 0.50 0.50 1.002 Total audios 6.79 3.40 –.00 1.003 Age 33.96 9.85 –.12 –.06 1.004 Sex 0.93 0.26 .05 .06 –.29* 1.005 White 0.21 0.41 –.10 –.14 .38** .146 Education 3.50 1.29 .09 –.12 .20 –.197 Marital status 0.57 0.50 .04 –.25* .09 .098 Supervisory status 0.22 0.42 .13 –.16 .09 –.129 Hours per week 39.74 6.07 –.09 .23 .20 –.28*

10 Clinical job 0.53 0.50 .17 –.03 .06 .1811 Organization tenure 4.83 6.44 –.13 –.08 .42*** .0012 Job tenure 2.56 3.24 .02 –.02 .35** .1613 Patients per day 22.65 22.86 .03 .27* –.30** .1314 Trait mindfulness .88 4.58 0.81 .10 .13 –.07 –.29*15 Conscientiousness .72 5.81 0.96 –.05 .26* .06 –.1616 Neuroticism .39 3.38 1.01 –.16 .10 –.20 .40***17 Extraversion .76 4.33 1.39 –.03 –.07 –.12 –.0918 Agreeableness .62 5.93 0.84 –.14 –.14 .19 –.2219 Openness .61 5.20 0.96 .01 .11 .02 –.1620 Task self-efficacy .91 6.23 0.73 .02 –.04 .09 –.2221 Mental health .90 3.60 0.68 .13 –.05 .19 –.34**22 Overall health 3.07 1.04 .09 .05 .20 –.1923 Job satisfaction .84 5.65 1.02 .07 –.05 .15 –.1724 Job stress .67 4.40 1.35 –.13 –.09 –.12 .1425 Emotional exhaustion .85 3.03 1.25 –.13 –.04 –.07 .2326 Fatigue .81 2.84 1.08 –.27* –.08 –.15 .27*27 Attentiveness .91 5.99 0.81 .13 .01 –.07 –.0628 Perspective taking .86 5.88 0.88 .02 .11 –.05 –.0229 Response flexibility .82 5.68 0.85 .09 .05 –.03 –.12

30Patient centered behavior .94 5.86 0.77 .09 .06 –.06 –.07

31Core job performance .80 6.30 0.66 –.06 .07 .07 .10

32 Proactive patient care .85 5.62 1.24 .10 .02 .02 –.2033 Job autonomy .76 5.62 1.05 –.05 –.01 .13 –.08

34Perceived workgroup patient care climate .88 3.78 0.76 .03 .10 –.03 –.08

35 Supervisor support .87 5.76 1.06 –.15 .13 .09 –.1436 Coworker support .86 3.39 1.20 –.14 .00 –.01 .19

112

Table 5 (Continued)

5 6 7 8 9 10 115 White 1.006 Education .23 1.007 Marital status .11 .14 1.008 Supervisory status .05 .18 .04 1.009 Hours per week –.16 .05 –.15 .15 1.0010 Clinical job .07 .28* .13 .10 –.10 1.0011 Organization tenure .29* –.19 –.04 –.03 –.02 .15 1.0012 Job tenure .15 –.34** –.01 –.06 –.04 .08 .51***13 Patients per day –.21 –.13 –.04 –.05 .04 –.07 –.1814 Trait mindfulness –.31** –.21 –.26* .09 .22 –.15 –.1715 Conscientiousness –.08 .03 –.06 –.17 –.01 –.01 –.2116 Neuroticism .01 –.27* –.04 –.11 –.20 .07 .2217 Extraversion –.04 .06 .18 .20 .16 –.02 –.0518 Agreeableness .16 .13 .16 .08 .01 –.02 –.0619 Openness .11 .06 –.09 .16 .19 –.12 .0320 Task self-efficacy –.02 –.08 –.15 –.08 .27* –.31** .0921 Mental health .04 .12 –.27* .04 .11 .02 –.0822 Overall health .20 .50*** –.06 .09 .19 .09 –.1523 Job satisfaction .17 .21 .04 –.05 –.05 .12 .0524 Job stress –.05 .17 .09 .11 –.02 .13 .0125 Emotional exhaustion –.11 –.06 .23 –.08 .01 –.02 .1326 Fatigue .06 –.11 .25* –.11 –.16 .05 .1227 Attentiveness –.16 –.07 .10 –.04 –.17 .33** .0428 Perspective taking –.14 –.08 .08 –.13 –.11 .16 –.1029 Response flexibility –.22 –.14 –.01 –.01 –.12 .10 –.0230 Patient centered

behavior–.19 –.10 .07 –.07 –.15 .22 –.03

31 Core job performance

–.15 –.07 .02 –.18 .08 .16 –.04

32 Proactive patient care –.14 .05 –.14 .07 .00 .09 –.0633 Job autonomy .02 .25* .02 –.12 .01 –.14 –.1034 Perceived workgroup

patient care climate.05 .02 –.11 –.12 –.10 .24* .06

35 Supervisor support .16 .06 .06 .11 .02 .05 .1636 Coworker support .13 –.00 –.05 –.11 .12 .18 .07

113

Table 5 (Continued)

12 13 14 15 16 17 1812 Job tenure 1.0013 Patients per day –.03 1.0014 Trait mindfulness –.16 .03 1.0015 Conscientiousness –.00 .19 .25* 1.0016 Neuroticism .16 .01 –.24* –.36** 1.0017 Extraversion –.14 .18 .11 –.04 –.04 1.0018 Agreeableness –.06 –.19 .23* .19 –.14 .19 1.0019 Openness –.01 –.16 .19 –.07 –.17 .28* .25*20 Task self-efficacy .08 .09 .16 .17 –.14 .34** .32**21 Mental health .05 –.24* .48*** .24* –.47*** –.01 .26*22 Overall health –.23 –.06 .21 .26* –.38*** .04 .1723 Job satisfaction –.02 –.26* .25* .27* –.25* –.03 .43***24 Job stress –.03 .07 –.29* –.02 .28* .05 –.1325 Emotional exhaustion .08 .10 –.39*** –.17 .35** .02 –.26*26 Fatigue .01 .11 –.52*** –.20 .39*** –.07 –.23*27 Attentiveness .13 .28* .15 .18 –.03 .13 .1828 Perspective taking .17 .16 .09 .23* –.10 .11 .27*29 Response flexibility .20 .26* .26* .16 .03 .07 .06

30Patient centered behavior .18 .26* .18 .21 –.03 .11 .19

31 Core job performance .10 .18 .18 .34** –.05 .16 .0832 Proactive patient care .01 –.03 .22 .27* –.02 .09 .1033 Job autonomy .13 –.23* .06 .09 –.01 –.04 .35**

34Perceived workgroup patient care climate –.07 –.16 .21 .18 –.16 –.15 .22

35 Supervisor support .04 –.06 .09 .16 –.06 .03 .48***36 Coworker support .03 –.12 –.16 .04 .22 .07 .26*

114

Table 5 (Continued)

19 20 21 22 23 24 2519 Openness 1.0020 Task self-efficacy .38*** 1.0021 Mental health .17 .14 1.0022 Overall health .02 .12 .40*** 1.0023 Job satisfaction .17 .18 .37** .40*** 1.0024 Job stress –.10 –.14 –.34** –.08 –.18 1.0025 Emotional exhaustion –.05 –.09 –.45*** –.35** –.61*** .57*** 1.0026 Fatigue –.14 –.24* –.69*** –.51*** –.32** .31** .45***27 Attentiveness .11 .27* .03 .08 .14 .01 .0328 Perspective taking .11 .20 .04 .09 .21 .02 .0529 Response flexibility –.00 .24* .10 .11 –.05 –.05 .1030 Patient centered

behavior.08 .26* .06 .10 .11 –.01 .07

31 Core job performance

.05 .34** –.01 .15 .19 –.03 –.01

32 Proactive patient care .19 .32** .07 .14 .12 .04 –.0433 Job autonomy .22 .22 .20 .14 .33** .12 –.1034 Perceived workgroup

patient care climate.14 .12 .24* .18 .53*** –.17 –.34**

35 Supervisor support .07 .20 .09 .16 .43*** –.03 –.1936 Coworker support –.08 .08 –.13 .06 .15 .02 –.04

115

Tabl

e 5

(Con

tinue

d)

2627

2829

3031

3233

3435

3626

Fatig

ue1.

0027

Atte

ntiv

enes

s–.

141.

0028

Pers

pect

ive

taki

ng–.

20.7

4***

1.00

29R

espo

nse

flexi

bilit

y–.

27*

.81*

**.7

0***

1.00

30Pa

tient

cen

tere

d be

havi

or–.

22.9

4***

.89*

**.9

1***

1.00

31C

ore

job

perf

orm

ance

–.11

.27*

.34*

*.2

6*.3

2**

1.00

32Pr

oact

ive

patie

nt c

are

–.22

.27*

.27*

.31*

*.3

1**

.55*

**1.

0033

Job

auto

nom

y–.

20.1

1.2

8*.1

2.1

9.1

1.3

2**

1.00

34Pe

rcei

ved

wor

kgro

up

patie

nt c

are

clim

ate

–.22

.40*

**.3

5**

.27*

.37*

*.1

3.1

9.3

0*1.

00

35Su

perv

isor

supp

ort

–.15

.19

.25*

.12

.20

.08

.12

.30*

.51*

**1.

0036

Cow

orke

r sup

port

.13

–.03

–.05

–.13

–.07

–.06

–.05

.01

.08

.20

1.00

Not

e.N

= 67

-72

parti

cipa

nts.

Con

ditio

n: 1

= tr

eatm

ent,

0 =

activ

e co

ntro

l. To

tal a

udio

s = T

otal

num

ber o

f aud

ios a

ttend

ed d

urin

g in

terv

entio

n. S

ex: 1

= fe

mal

e, 0

=

mal

e. W

hite

: 1 =

Whi

te, 0

= o

ther

. Edu

catio

n: 1

= so

me

high

scho

ol (g

rade

11

or le

ss, 2

= g

radu

ated

from

hig

h sc

hool

or G

.E.D

., 3

= so

me

colle

ge o

r tec

hnic

al tr

aini

ng b

eyon

d hi

gh sc

hool

(1-3

yea

rs),

4 =

grad

uate

d fr

om c

olle

ge (B

.A.,

B.S

., or

oth

er B

ache

lor’

s deg

ree,

5

= so

me

grad

uate

scho

ol, 6

= g

radu

ate

or p

rofe

ssio

nal d

egre

e (M

BA

, Mas

ter’

s, Ph

.D.,

J.D.,

etc.

Mar

ital s

tatu

s: 1

= m

arrie

d, sa

me-

sex

dom

estic

par

tner

, or l

ivin

g w

ith a

sign

ifica

nt o

ther

or p

artn

er, 0

= si

ngle

, div

orce

d, se

para

ted,

or w

idow

ed. S

uper

viso

ry st

atus

: 1 =

su

perv

ise

othe

r em

ploy

ees,

0 =

othe

rwis

e. H

ours

per

wee

k: H

ours

wor

ked

in a

typi

cal w

eek.

Clin

ical

job:

1 =

clin

ical

job,

0 =

ad

min

istra

tive

job.

Org

aniz

atio

n te

nure

and

job

tenu

re a

re in

yea

r. Pa

tient

s per

day

: Num

ber o

f pat

ient

s see

or a

ssis

t in

a ty

pica

l w

orkd

ay.

*p

< .0

5. *

* p

< .0

1. *

** p

< .0

01. T

wo-

taile

d te

st.

116

Tabl

e 6

Mea

n, S

tand

ard

Dev

iatio

n, C

ronb

ach’

s , a

nd Z

ero-

Ord

er C

orre

latio

ns o

f Par

ticip

ant R

epor

ted

Dai

ly S

tudy

Var

iabl

es in

the

Part

icip

ant E

nd-o

f-Wor

kday

Sur

vey

MSD

12

34

56

1M

indf

ulne

ss0.

854.

450.

791.

002

Moo

d3.

830.

93.2

8***

1.00

3Jo

b sa

tisfa

ctio

n3.

790.

94.4

0***

.63*

**1.

004

Job

perf

orm

ance

0.92

3.39

0.67

.28*

**.4

3***

.45*

**1.

005

Proa

ctiv

e pa

tient

car

e3.

431.

04.2

0***

.24*

**.2

2***

.24*

**1.

006

Atte

ntiv

enes

s0.

884.

040.

95.2

8***

.37*

**.4

1***

.29*

**.3

7***

1.00

7Pe

rspe

ctiv

e ta

king

0.94

3.94

0.96

.28*

**.3

9***

.43*

**.3

1***

.47*

**.8

1***

8R

espo

nse

flexi

bilit

y0.

843.

741.

00.2

1***

.34*

**.3

7***

.28*

**.4

7***

.77*

**9

Patie

nt c

ente

red

beha

vior

0.

943.

910.

90.2

8***

.40*

**.4

4***

.32*

**.4

9***

.93*

**10

Stre

ss2.

471.

16–.

28**

*–.

30**

*–.

30**

*–.

03–.

07–.

10**

11Em

otio

nal e

xhau

stio

n2.

711.

14–.

24**

*–.

34**

*–.

36**

*–.

08*

–.05

–.11

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ue2.

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k ov

erlo

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p qu

ality

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ical

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ivity

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t2.

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117

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e6

(con

tinue

d)

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1112

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pect

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Res

pons

e fle

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.76*

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nt c

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selin

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d in

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ly o

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wo-

taile

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118

Tabl

e 7

Mea

n, S

tand

ard

Dev

iatio

n, C

ronb

ach’

s , a

nd Z

ero-

Ord

er C

orre

latio

ns o

f Stu

dy V

aria

bles

in th

e Pa

tient

Sur

vey

MSD

12

34

56

78

910

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rvey

lang

uage

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451.

002

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ning

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011.

003

Firs

t enc

ount

er

1.32

0.47

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–.02

1.00

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now

n pa

rtici

pant

0.50

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–.08

***

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1.00

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me

with

par

ticip

ant

12.0

816

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1.00

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ttent

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pect

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nt sa

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e.N

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rven

tion

perio

ds (D

ay 1

to 1

5).

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ey la

ngua

ge: 1

= E

nglis

h, 0

= S

pani

sh. M

orni

ng: 0

= e

ncou

nter

by

noon

(inc

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= e

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r noo

n. F

irst

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unte

r: 0

= fir

st p

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ipan

t enc

ount

ered

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ing

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t, 1

= se

cond

par

ticip

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g vi

sit.

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wn

parti

cipa

nt: 1

=tre

ated

by

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ticip

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= n

o. T

ime

with

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ticip

ant:

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utes

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t with

this

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ticip

ant.

Atte

ntiv

enes

s, pe

rspe

ctiv

e ta

king

, res

pons

e fle

xibi

lity,

pat

ient

cen

tere

d be

havi

or, a

nd p

atie

nt sa

tisfa

ctio

n w

ere

repo

rted

bypa

tient

s.

*p

<.0

5. *

* p

< .0

1. *

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< .0

01. T

wo-

taile

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st.

119

Table 8Pre- and Post-Intervention Means and Standard Deviations for Study Variables by Condition

Pre-intervention Post-interventionVariable Condition N M SD N M SDMindfulness Treatment 35 4.41 0.73 35 4.49 0.63

Active control 34 4.26 0.69 32 4.39 0.71Mood Treatment 35 3.80 0.63 35 3.89 0.63

Active control 34 3.73 0.62 32 3.79 0.65Job satisfaction Treatment 35 3.73 0.76 35 3.81 0.75

Active control 34 3.81 0.71 32 3.71 0.75Job performance Treatment 35 3.27 0.58 35 3.48 0.60

Active control 34 3.28 0.40 32 3.37 0.47Proactive patient care Treatment 32 3.36 0.76 30 3.58 0.88

Active control 29 3.11 1.10 26 3.33 0.92Attentiveness Treatment 35 3.91 0.82 35 3.95 0.91

Active control 34 3.95 0.91 32 4.07 0.83Perspective taking Treatment 35 3.81 0.92 35 3.98 0.80

Active control 34 3.77 0.86 32 4.01 0.89Response flexibility Treatment 35 3.54 0.84 35 3.79 0.90

Active control 34 3.54 0.89 32 3.80 0.95Patient centered behavior Treatment 35 3.75 0.80 35 3.91 0.82

Active control 34 3.76 0.85 32 3.96 0.84Stress Treatment 35 2.42 0.88 35 2.35 0.78

Active control 34 2.61 0.87 32 2.54 0.81Emotional exhaustion Treatment 35 2.71 0.85 35 2.56 0.71

Active control 34 2.83 0.88 32 2.78 0.90Fatigue Treatment 35 2.99 0.77 35 2.67 0.65

Active control 34 3.09 0.91 32 2.87 0.78Sleep quality Treatment 35 3.35 0.86 34 3.47 0.64

Active control 34 3.03 0.74 32 3.22 0.48Physical activity Treatment 35 2.61 0.88 34 2.91 0.94

Active control 34 2.35 0.79 32 2.57 0.72Diet Treatment 35 2.96 0.83 34 3.15 0.81

Active control 34 2.73 0.73 32 2.92 0.64Work overload Treatment 35 2.36 0.82 35 2.30 0.83

Active control 34 2.51 0.69 32 2.57 0.79Job autonomy Treatment 35 3.16 0.91 35 2.98 1.01

Active control 34 3.41 0.88 32 3.43 0.99Attentivenessa Treatment 25 6.65 0.24 26 6.67 0.25

Active control 23 6.72 0.24 21 6.62 0.25Perspective takinga Treatment 25 6.62 0.27 26 6.58 0.30

Active control 23 6.69 0.30 21 6.57 0.28

120

Response flexibilitya Treatment 25 6.60 0.30 26 6.58 0.30Active control 23 6.65 0.29 21 6.55 0.27

Patient centered behaviora Treatment 25 6.62 0.26 26 6.61 0.28Active control 23 6.69 0.27 21 6.58 0.26

Patient satisfactiona Treatment 25 4.86 0.13 26 4.85 0.15Active control 23 4.86 0.16 21 4.85 0.11

Note. For each variable, pre-intervention score for each participant was computed by averaging daily observations (or encounters) across the 5 days before intervention for thatparticipant. Post-intervention score for each participant was computed by averaging daily observations (or encounters) across the 10 days during intervention for that participant. Condition mean for each variable was computed by averaging the variable scores across participants in that condition.a Patient-rated variables

121

Tabl

e 9

Sum

mar

y of

Res

ults

from

Mul

tilev

el M

odel

ing

(MLM

) and

Lat

ent G

row

th M

odel

ing

(LG

M) o

f Dai

ly O

utco

mes

dur

ing

Inte

rven

tion

for

Part

icip

ants

Atte

ndin

g At

Lea

st O

ne A

udio

M

LMLG

M: 1

0-D

ay M

odel

LGM

: 5-W

ave

Mod

elIn

terv

entio

n Ef

fect

Aud

io

Effe

ctTr

eatm

ent

Act

ive

Con

trol

Diff

eren

ceTr

eatm

ent

Act

ive

Con

trol

Diff

eren

ce

Min

dful

ness

No

No

Posi

tive

nsY

esPo

sitiv

ens

No

Job

satis

fact

ion

No

No

nsns

No

nsns

No

Job

perf

orm

ance

No

No

Posi

tive

nsY

esPo

sitiv

ens

No

Proa

ctiv

e pa

tient

car

eN

oY

esns

nsY

esns

nsY

esPa

tient

cen

tere

d be

havi

orN

oN

oPo

sitiv

ens

No

Posi

tive

nsN

oSt

ress

No

No

nsns

No

nsns

No

Emot

iona

l exh

aust

ion

No

No

nsns

No

nsns

Yes

Fatig

ueN

oN

oN

egat

ive

nsN

oN

egat

ive

nsN

oPa

tient

rate

d pa

tient

ce

nter

ed b

ehav

ior

No

Yes

nsns

No

nsns

No

Patie

nt sa

tisfa

ctio

nN

oN

ons

nsY

esPo

sitiv

e ns

No

Not

e. F

or m

ultil

evel

mod

elin

g of

pat

ient

rate

d pa

tient

cen

tere

d be

havi

or a

nd p

atie

nt sa

tisfa

ctio

n, e

ncou

nter

-leve

l ins

tead

of d

ay-le

vel

outc

ome

was

use

d.

For i

nter

vent

ion

effe

ct c

olum

n in

MLM

: No

= pa

rtici

pant

-leve

l mea

n da

ily o

utco

me

did

not d

iffer

bet

wee

n tre

atm

ent a

nd a

ctiv

e co

ntro

l con

ditio

ns. F

or a

udio

eff

ect c

olum

n in

MLM

: Yes

= T

otal

aud

ios a

ttend

ed m

oder

ated

the

effe

ct o

f con

ditio

n on

the

outc

ome

such

that

com

pare

d w

ith a

ctiv

e co

ntro

l, tre

atm

ent l

ed to

a h

ighe

r lev

el o

f the

out

com

e fo

r par

ticip

ants

atte

ndin

g a

high

er a

mou

nt o

f au

dios

. No

= To

tal a

udio

s atte

nded

did

not

mod

erat

e th

e ef

fect

of c

ondi

tion

on th

e ou

tcom

e.

For t

reat

men

t and

act

ive

cont

rol c

olum

ns in

LG

M: P

ositi

ve =

sign

ifica

nt li

near

incr

ease

of d

aily

out

com

e du

ring

inte

rven

tion.

Neg

ativ

e =

sign

ifica

nt li

near

dec

line

of d

aily

out

com

e du

ring

inte

rven

tion.

ns =

no

chan

ge o

f dai

ly o

utco

me

durin

g in

terv

entio

n. F

or

diff

eren

ce c

olum

n in

LG

M: Y

es =

trea

tmen

t gro

wth

traj

ecto

ry w

as d

iffer

ent f

rom

act

ive

cont

rol g

row

th tr

ajec

tory

. No

= tre

atm

ent

grow

thtra

ject

ory

was

not

diff

eren

t fro

m a

ctiv

e co

ntro

l gro

wth

traj

ecto

ry.

122

Table 10Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Mindfulness over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.51*** .41 1.45** .47Outcome at baseline .68*** .09 .66*** .08Condition –.01 .10 –.06 .38Total number of audios attended (TNAA) .02 .04Condition x TNAA .01 .05Residual variance .12*** .03 .12*** .03Within-participant levelResidual variance .34*** .07 .34*** .07Model deviance 939.46 938.60

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily mindfulness across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

123

Table 11Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Job Satisfaction over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.19** .36 .94 .66Outcome at baseline .68*** .09 .68*** .10Condition .13 .11 .47 .46Total number of audios attended (TNAA) .03 .05Condition x TNAA –.04 .06Residual variance .13*** .03 .13*** .03Within-participant levelResidual variance .55*** .07 .55*** .07Model deviance 1,160.32 1,159.62

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily job satisfaction across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

124

Table 12Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Job Performance over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.00** .37 .82† .44Outcome at baseline .72*** .11 .73*** .10Condition .15 .10 .56 .36Total number of audios attended (TNAA) .02 .04Condition x TNAA –.05 .05Residual variance .13** .04 .12** .04Within-participant levelResidual variance .21*** .03 .22*** .03Model deviance 741.91 740.10

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily job performance across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

† p < .10. * p < .05. ** p < .01. *** p < .001. Two-tailed test.

125

Table 13Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Proactive Patient Care over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept .60† .32 1.23** .47Outcome at baseline .82*** .09 .78*** .09Condition .21 .15 –.78† .40Total number of audios attended (TNAA) –.06† .03Condition x TNAA .13* .05Residual variance .24*** .04 .22*** .04Within-participant levelResidual variance .37*** .04 .37*** .04Model deviance 822.46 818.40

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noonthe following day.

N = 396 daily observations nested with 54 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily proactive patient care across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

† p < .10. * p < .05. ** p < .01. *** p < .001. Two-tailed test.

126

Table 14Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Participant-Rated Patient Centered Behavior over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept .53* .21 .88* .36Outcome at baseline .90*** .05 .88*** .05Condition .01 .09 –.45 .28Total number of audios attended (TNAA) –.03 .03Condition x TNAA .06 .04Residual variance .11*** .03 .10*** .02Within-participant levelResidual variance .17*** .04 .17*** .04Model deviance 637.04 634.94

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily participant rated patient centered behavior across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

127

Table 15Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Stress over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.06*** .24 .78* .39Outcome at baseline .58*** .09 .57*** .09Condition –.13 .15 –.29 .47Total number of audios attended (TNAA) .04 .05Condition x TNAA .02 .06Residual variance .22*** .05 .21*** .05Within-participant levelResidual variance .86*** .09 .86*** .09Model deviance 1380.88 1378.00

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily stress across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

128

Table 16Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Emotional Exhaustion over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.02*** .22 .92* .43Outcome at baseline .64*** .08 .63*** .08Condition –.17 .14 –.39 .53Total number of audios attended (TNAA) .01 .06Condition x TNAA .03 .06Residual variance .18** .05 .18** .05Within-participant levelResidual variance .83*** .10 .83*** .10Model deviance 1,359.08 1,357.72

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily emotional exhaustion across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

129

Table 17Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Daily Fatigue over Intervention Period

Model 1 Model 2Parameter Estimate SE Estimate SEBetween-participant levelIntercept 1.33*** .27 1.33** .47Outcome at baseline .51*** .09 .52*** .09Condition –.17 .13 –.37 .55Total number of audios attended (TNAA) –.001 .05Condition x TNAA .03 .07Residual variance .14** .04 .14** .04Within-participant levelResidual variance .93*** .10 .93*** .11Model deviance 1,402.80 1,402.22

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome. Daily outcome was included in the analyses if it was responded to on the same day a daily survey was sent or by noon the following day.

N = 489 daily observations nested with 64 participants. Robust maximum likelihood estimator was used to produce standard errors that were robust to non-normality and non-independence of observations (Muthén & Muthén, 2012; Preacher et al., 2010). Model deviance = –2 x log-likelihood of the full maximum-likelihood estimate. Smaller deviance indicates better model fit.

Outcome at baseline refers to averaged daily fatigue across the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

130

Table 18Cross-Classified Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Encounter-Level Patient Rated Patient Centered Behavior over Intervention Period

Model 1 Model 2Parameter Estimate 95% CI Estimate 95% CIParticipant levelIntercept 4.25* [2.53, 6.00] 5.90* [3.05, 8.09]Outcome at baseline .34* [.06, .59] .19 [–.12, .51]Condition .07 [–.05, .20] –.69* [–1.41, –.05]Total number of audios attended (TNAA)

–.07* [–.13, –.01]

Condition x TNAA .09* [.01, .16]Residual variance .01* [.004, .04] .01* [.004, .03]Patient levelResidual variance .58* [.49, .66] .58* [.50, .67]Encounter levelResidual variance .24* [.21, .27] .24* [.21, .27]

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome.

N = 965 observations nested with 41 participants and 690 patients. Bayesian estimation was used by default in Mplus (Muthén & Muthén, 2012).

Outcome at baseline refers to averaged patient rated patient centered behavior across encounters over the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

131

Table 19Cross-Classified Multilevel Modeling Results for the Effect of Experimental Condition and Total Number of Audios Attended (TNAA) on Encounter-Level Patient Satisfaction over Intervention Period

Model 1 Model 2Parameter Estimate 95% CI Estimate 95% CIParticipant levelIntercept 5.35* [4.06, 6.50] 5.35* [4.00, 6.96]Outcome at baseline –.11 [–.35, .15] –.06 [–.38, .23]Condition .02 [–.02, .06] –.23 [–.55, .12]Total number of audios attended (TNAA)

–.03 [–.06, .01]

Condition x TNAA .03 [–.01, .06]Residual variance .001* [.000, .005] .001* [.000, .010]Patient levelResidual variance .13* [.11, .17] .13* [.11, .15]Encounter levelResidual variance .07* [.06, .08] .07* [.06, .08]

Note. Each model included individuals who attended at least one audio over the 10-day intervention period and had nonmissing baseline outcome.

N = 988 observations nested with 41 participants and 711 patients. Bayesian estimation was used by default in Mplus (Muthén & Muthén, 2012).

Outcome at baseline refers to averaged patient satisfaction across encounters over the 5 days before the intervention started for each participant.

Condition: 1 = treatment, 0 = active control. Estimates were unstandardized coefficients.

* p < .05. ** p < .01. *** p < .001. Two-tailed test.

132

Table 20Key Parameter Estimates for Final Models of Normative Growth Trajectory of Daily Outcomes

Initial status factor Linear growth factorOutcome N Mean Variance Mean Variance CovarianceMindfulness 64 4.35*** 0.38*** 0a 0a 0a

Job satisfaction 64 3.78*** 0.42*** 0a 0a 0a

Job performance 64 3.29*** 0.20*** 0a 0.01* 0a

Proactive patient care 58 3.31*** 0.64*** 0a 0a 0a

Participant-Rated Patient centered behavior

64 3.81*** 0.57*** 0a 0.02* 0a

Stress 64 2.54*** 0.84** 0a 0.09** –0.15*Emotional exhaustion 64 2.82*** 0.45** 0a 0.03† 0a

Fatigue 64 3.00*** 0.55*** 0a 0a 0a

Patient-rated patient centered behavior

46 6.59*** .03† 0a 0a 0a

Patient satisfaction 46 4.87*** .004 0a 0a 0a

Note. N refers to number of participants.

Normative growth trajectories were derived from repeated measures of each outcome

over the 5 days (4 days for sleep quality, physical activity, and diet) before the

intervention started using participants in both treatment and active control conditions.

Participants who attended at least one audio were included in the analyses.

Covariance refers to covariance between initial status factor and linear growth factor. a Parameter was fixed.

† p < .10. * p < .05. ** p < .01. *** p < .001. Two-tailed test.

133

Table 21Fit Statistics for Linear Growth Model of Daily Outcome by Condition Using 10 Days of Repeated Measures during InterventionCondition N df 2 CFI RMSEA SRMRMindfulnessTreatment 32 61 150.61 .60 .21 .74Active Control 32 63 181.23 .57 .24 .30Job satisfactionTreatment 32 61 183.43 .53 .25 .21Active Control 32 61 176.58 .36 .24 .26Job performanceTreatment 32 61 136.60 .74 .20 .24Active Control 32 61 238.54 .38 .30 .43Proactive patient careTreatment 30 61 161.98 .65 .24 .16Active Control 29 61 263.72 .47 .34 .30Participant Rated Patient centered behaviorTreatment 32 61 155.25 .78 .22 .22Active Control 32 61 138.24 .85 .20 .14StressTreatment 32 61 85.09 .82 .11 .20Active Control 32 61 167.18 .35 .23 .25Emotional exhaustionTreatment 32 61 110.36 .63 .16 .23Active Control 32 61 164.16 .49 .23 .31FatigueTreatment 32 61 131.40 .41 .19 .25Active Control 32 61 157.76 .41 .22 .27Patient rated patient centered behaviorTreatment 26 61 208.26 .04 .31 .85Active Control 25 63 235.50 .13 .33 .76Patient satisfactionTreatment 26 64 203.19 .00 .29 4.70Active Control 25 63 188.46 .00 .28 .87

Note. Participants attending at least one audio were included. For sleep quality, physical activity, and diet, 8 days of repeated measures were used because no measures were taken for these outcomes on Fridays.

134

Table 22Parameter Estimates of Linear Growth Model of Daily Outcome by Condition Using 10 Days of Repeated Measures during Intervention

Condition Initial Status Mean

Initial Status Variance

Growth Rate Mean

Growth Rate Variance

Covariance (Initial Status and Growth Rate)

MindfulnessTreatment 4.39*** 0.45** 0.02* 0.001 –0.01†Active Control 4.48*** 0.32*** 0.001 0a 0a

Job satisfactionTreatment 3.76*** .39** .02 .002 .01Active Control 3.79*** .30** .001 .001 –.004Job performanceTreatment 3.35*** .33*** .03** .002** –.004Active Control 3.31*** .17** .004 .001 –.004Proactive patient careTreatment 3.42*** .45** .02 .002 .02Active Control 3.22*** 1.24** –.004 .001 –.04†Participant Rated Patient centered behaviorTreatment 3.84*** .48*** .03** .002* .01Active Control 3.90*** .70*** .01 .001 –.01StressTreatment 2.47*** .67*** –.01 .01** –.04*Active Control 2.59*** .54** –.01 .01† –.02Emotional exhaustionTreatment 2.78*** .60*** –.02 .01* –.04*Active Control 2.76*** .53** .01 < .001 .004FatigueTreatment 2.96*** .35* –.05* .004 –.01Active Control 3.04*** .67** –.03 .01† –.03Patient rated patient centered behaviorTreatment 6.58*** .03 .004 < .001 –.001Active Control 6.67*** .05** .001 0a 0a

Patient satisfactionTreatment 5.00*** 0a –.01** 0a 0a

Active Control 4.86*** 0a .002 <.001 0a

Note. Participants attending at least one audio were included. For sleep quality, physical activity, and diet, 8 days of repeated measures were used because no measures were taken for these outcomes on Fridays. a Parameter was fixed at zero.

† p < .10. * p < .05. ** p < .01. *** p < .001. Two-tailed test.

135

Table 23Fit Statistics for Linear Growth Model of Daily Outcome by Condition Using 5 Waves of Repeated Measures during InterventionCondition N df 2 CFI RMSEA SRMRMindfulnessTreatment 32 18 24.46 .95 .11 .53Active Control 32 16 32.03 .87 .18 .25Job satisfactionTreatment 32 16 16.75 .99 .04 .19Active Control 32 16 19.03 .95 .08 .17Job performanceTreatment 32 16 16.06 1.00 .01 .21Active Control 32 16 26.69 .89 .14 .22Proactive patient careTreatment 30 16 20.41 .97 .10 .09Active Control 29 18 29.84 .91 .15 .22Patient centered behaviorTreatment 32 16 23.41 .97 .12 .12Active Control 32 16 17.74 .99 .06 .11StressTreatment 32 16 15.99 1.00 .00 .13Active Control 32 16 28.01 .76 .15 .15Emotional exhaustionTreatment 32 16 15.73 1.00 .00 .11Active Control 32 18 20.99 .96 .07 .12FatigueTreatment 32 16 24.57 .83 .13 .18Active Control 32 16 23.86 .86 .12 .19Patient rated patient centered behaviorTreatment 26 16 14.75 1.00 .00 .56Active Control 25 18 33.43 .54 .19 1.26Patient satisfactionTreatment 26 18 23.48 .34 .11 .46Active Control 25 18 22.11 .00 .10 .51

Note. Participants attending at least one audio were included. For sleep quality, physical activity, and diet, 4 waves of repeated measures were used because no measures were taken for these outcomes on Fridays.

136

Table 24Parameter Estimates of Linear Growth Model of Daily Outcome by Condition Using 5 Waves of Repeated Measures during Intervention

Condition Initial Status Mean

Initial Status Variance

Growth Rate Mean

Growth Rate Variance

Covariance (Initial Status and Growth Rate)

MindfulnessTreatment 4.37*** 0.38*** 0.03 0a 0a

Active Control 4.36*** 0.40** 0.02 0.003 –0.01Job satisfactionTreatment 3.73*** .43** .03 .004 .01Active Control 3.78*** .33** –.02 .003 –.003Job performanceTreatment 3.32*** .33*** .07** .01* –.02Active Control 3.30*** .14** .01 .01† –.01Proactive patient careTreatment 3.43*** .44** .03 .01 .03Active Control 3.20*** .93** –.01 0a 0a

Patient centered behaviorTreatment 3.82*** .49*** .06** .01 .01Active Control 3.87*** .67*** .03 .002 –.003StressTreatment 2.51*** .68*** –.03 .03** –.08*Active Control 2.58*** .45* –.004 .004 –.01Emotional exhaustionTreatment 2.81*** .65*** –.04 .02* –.07*Active Control 2.78*** .54** .02 0a 0a

FatigueTreatment 3.00*** .44** –.09** .02 –.04Active Control 3.02*** .55** –.04 .01 –.04Patient rated patient centered behaviorTreatment 6.59*** .03 .001 .001 < .001Active Control 6.67*** .05** .01 0a 0a

Patient satisfactionTreatment 4.85*** 0a .02* <.001 0a

Active Control 4.84*** 0a .01 <.001 0a

Note. Participants attending at least one audio were included. For sleep quality, physical activity, and diet, 4 waves of repeated measures were used because no measures were taken for these outcomes on Fridays. a Parameter was fixed at zero.

† p < .10. * p < .05. ** p < .01. *** p < .001. Two-tailed test.

137

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io o

ver t

he in

terv

entio

n pe

riod.

N=

32 in

trea

tmen

t con

ditio

n; N

= 32

in a

ctiv

e co

ntro

l con

ditio

n.

aPa

ram

eter

was

fixe

d.

Estim

ates

wer

e un

stan

dard

ized

coe

ffic

ient

s.

†p

< .1

0. *

p<

.05.

**

p<

.01.

***

p<

.001

. Tw

o-ta

iled

test

.

143

Tabl

e 31

Two-

Gro

up L

inea

r Gro

wth

Mod

els f

or D

aily

Em

otio

nal E

xhau

stio

n ov

er In

terv

entio

n Pe

riod

Usi

ng 1

0-D

ay a

nd 5

-Wav

e Re

peat

ed

Mea

sure

s10

-Day

Mod

el5-

Wav

e M

odel

Trea

tmen

t gro

upA

ctiv

e co

ntro

l gro

upTr

eatm

ent g

roup

Act

ive

cont

rol g

roup

Para

met

erEs

timat

eSE

Estim

ate

SEEs

timat

eSE

Estim

ate

SEN

orm

ativ

e de

velo

pmen

tIn

itial

stat

us m

ean

2.77

***

.11

2.77

***

.11

2.80

***

.11

2.80

***

.11

Initi

al st

atus

var

ianc

e.5

7***

.12

.57*

**.1

2.6

0***

.12

.60*

**.1

2In

terv

entio

n ef

fect

Line

ar g

row

th fa

ctor

mea

n–.

02.0

2.0

1.0

2–.

04.0

3.0

2.0

3Li

near

gro

wth

fact

or v

aria

nce

.01*

.002

< .0

01.0

02.0

2*.0

10a

0a

Cov

aria

nce

(initi

al st

atus

and

lin

ear g

row

th fa

ctor

)–.

03*

.02

.003

.01

–.07

*.0

30a

0a

Not

e. E

ach

mod

el in

clud

ed in

divi

dual

s who

atte

nded

at l

east

one

aud

io o

ver t

he in

terv

entio

n pe

riod.

N=

32 in

trea

tmen

t con

ditio

n; N

= 32

in a

ctiv

e co

ntro

l con

ditio

n.

aPa

ram

eter

was

fixe

d.

Estim

ates

wer

e un

stan

dard

ized

coe

ffic

ient

s.

*p

< .0

5. *

* p

< .0

1. *

** p

< .0

01. T

wo-

taile

d te

st.

144

Tabl

e 32

Two-

Gro

up L

inea

r Gro

wth

Mod

els f

or D

aily

Fat

igue

ove

r Int

erve

ntio

n Pe

riod

Usi

ng 1

0-D

ay a

nd 5

-Wav

e Re

peat

ed M

easu

res

10-D

ay M

odel

5-W

ave

Mod

elTr

eatm

ent g

roup

Act

ive

cont

rol g

roup

Trea

tmen

t gro

upA

ctiv

e co

ntro

l gro

upPa

ram

eter

Estim

ate

SEEs

timat

eSE

Estim

ate

SEEs

timat

eSE

Nor

mat

ive

deve

lopm

ent

Initi

al st

atus

mea

n3.

00**

*.1

03.

00**

*.1

03.

01**

*.1

03.

01**

*.1

0In

itial

stat

us v

aria

nce

.48*

**.1

3.4

8***

.13

.49*

**.1

3.4

9***

.13

Inte

rven

tion

effe

ctLi

near

gro

wth

fact

or m

ean

–.05

**.0

2–.

02.0

2–.

09**

.03

–.04

.03

Line

ar g

row

th fa

ctor

var

ianc

e.0

1†.0

03.0

1†.0

03.0

2†.0

1.0

1.0

1C

ovar

ianc

e (in

itial

stat

us a

nd

linea

r gro

wth

fact

or)

–.02

.02

–.02

.02

–.05

.03

–.03

.03

Not

e. E

ach

mod

el in

clud

ed in

divi

dual

s who

atte

nded

at l

east

one

aud

io o

ver t

he in

terv

entio

n pe

riod.

N=

32 in

trea

tmen

t con

ditio

n; N

= 32

in a

ctiv

e co

ntro

l con

ditio

n.

aPa

ram

eter

was

fixe

d.

Estim

ates

wer

e un

stan

dard

ized

coe

ffic

ient

s.

†p

< .1

0. *

p<

.05.

**

p<

.01.

***

p<

.001

. Tw

o-ta

iled

test

.

145

Tabl

e 33

Two-

Gro

up L

inea

r Gro

wth

Mod

els f

or D

aily

Pat

ient

Rat

ed P

atie

nt C

ente

red

Beha

vior

ove

r Int

erve

ntio

n Pe

riod

Usi

ng 1

0-D

ay a

nd 5

-W

ave

Repe

ated

Mea

sure

s10

-Day

Mod

el5-

Wav

e M

odel

Trea

tmen

t gro

upA

ctiv

e co

ntro

l gro

upTr

eatm

ent g

roup

Act

ive

cont

rol g

roup

Para

met

erEs

timat

eSE

Estim

ate

SEEs

timat

eSE

Estim

ate

SEN

orm

ativ

e de

velo

pmen

tIn

itial

stat

us m

ean

6.63

***

.04

6.63

***

.04

6.63

***

.04

6.63

***

.04

Initi

al st

atus

var

ianc

e.0

5**

.01

.05*

*.0

1.0

5**

.02

.05*

*.0

2In

terv

entio

n ef

fect

Line

ar g

row

th fa

ctor

mea

n<

.001

.01

.002

.01

–.01

.02

.01

.01

Line

ar g

row

th fa

ctor

var

ianc

e.0

01.0

010a

0a.0

02.0

10a

0a

Cov

aria

nce

(initi

al st

atus

and

lin

ear g

row

th fa

ctor

)–.

003

.003

0a0a

–.01

.01

0a0a

Not

e. E

ach

mod

el in

clud

ed in

divi

dual

s who

atte

nded

at l

east

one

aud

io o

ver t

he in

terv

entio

n pe

riod.

N=

26 in

trea

tmen

t con

ditio

n; N

= 25

in a

ctiv

e co

ntro

l con

ditio

n.

aPa

ram

eter

was

fixe

d.

Estim

ates

wer

e un

stan

dard

ized

coe

ffic

ient

s.

*p

< .0

5. *

* p

< .0

1. *

** p

< .0

01. T

wo-

taile

d te

st.

146

Tabl

e 34

Two-

Gro

up L

inea

r Gro

wth

Mod

els f

or D

aily

Pat

ient

Sat

isfa

ctio

n ov

er In

terv

entio

n Pe

riod

Usi

ng 1

0-D

ay a

nd 5

-Wav

e Re

peat

ed

Mea

sure

s10

-Day

Mod

el5-

Wav

e M

odel

Trea

tmen

t gro

upA

ctiv

e co

ntro

l gro

upTr

eatm

ent g

roup

Act

ive

cont

rol g

roup

Para

met

erEs

timat

eSE

Estim

ate

SEEs

timat

eSE

Estim

ate

SEN

orm

ativ

e de

velo

pmen

tIn

itial

stat

us m

ean

4.95

***

.18

4.95

***

.18

4.85

***

.02

4.85

***

.02

Initi

al st

atus

var

ianc

e0a

0a0a

0a0a

0a0a

0a

Inte

rven

tion

effe

ctLi

near

gro

wth

fact

or m

ean

–.00

1.0

2–.

01.0

2.0

2**

.01

.01

.01

Line

ar g

row

th fa

ctor

var

ianc

e<

.001

< .0

01<

.001

<.00

1<

.001

< .0

01<

.001

< .0

01C

ovar

ianc

e (in

itial

stat

us a

nd

linea

r gro

wth

fact

or)

0a0a

0a0a

0a0a

0a0a

Not

e. E

ach

mod

el in

clud

ed in

divi

dual

s who

atte

nded

at l

east

one

aud

io o

ver t

he in

terv

entio

n pe

riod.

N=

26 in

trea

tmen

t con

ditio

n; N

= 25

in a

ctiv

e co

ntro

l con

ditio

n.

aPa

ram

eter

was

fixe

d.

Estim

ates

wer

e un

stan

dard

ized

coe

ffic

ient

s.

*p

< .0

5. *

* p

< .0

1. *

** p

< .0

01. T

wo-

taile

d te

st.

147

Tabl

e 35

Mea

n G

row

th R

ate

Estim

ates

of T

reat

men

t and

Act

ive

Con

trol

Inte

rven

tion

in th

e Fi

nal T

wo-

Gro

up L

inea

r Gro

wth

Mod

el o

f Dai

ly

Out

com

e U

sing

10-

Day

and

5-W

ave

Repe

ated

Mea

sure

s dur

ing

Inte

rven

tion

for P

artic

ipan

ts A

ttend

ing

At L

east

Fiv

e Au

dios

10-D

ay M

odel

5-W

ave

Mod

elN

TN

CTr

eatm

ent

Act

ive

Con

trol

df2

Trea

tmen

tA

ctiv

e C

ontro

ldf

2

Min

dful

ness

2730

.02†

.002

15.

89*

.03

.01

3.9

7Jo

b sa

tisfa

ctio

n27

30.0

1.0

11

.03

.03

.001

11.

61Jo

b pe

rfor

man

ce27

30.0

3*.0

13

8.84

*.0

6*.0

13

3.83

Proa

ctiv

e pa

tient

car

e25

27.0

1–.

013

5.97

.02

–.01

13.

93*

Patie

nt c

ente

red

beha

vior

2730

.03*

**.0

13

3.44

.06*

*.0

33

2.98

Stre

ss27

30.0

01<

.001

30.

37–.

004

.002

30.

61Em

otio

nal e

xhau

stio

n27

30–.

02.0

13

4.21

–.04

.03

34.

42Fa

tigue

2730

–.04

*–.

03+

30.

71–.

07*

–.05

†3

1.35

Patie

nt ra

ted

patie

nt

cent

ered

beh

avio

r22

23.0

01.0

033

0.89

–.00

3.0

13

0.97

Patie

nt sa

tisfa

ctio

n22

23–.

003

–.01

+1

5.50

*.0

2**

.01

22.

40N

ote.

NT

and

NC

refe

r to

num

ber o

f par

ticip

ants

in th

e tre

atm

ent a

nd a

ctiv

e co

ntro

l con

ditio

n, re

spec

tivel

y.

Mea

n gr

owth

rate

est

imat

es fo

r tre

atm

ent a

nd a

ctiv

e co

ntro

l con

ditio

ns w

ere

from

two-

grou

p la

tent

gro

wth

mod

elin

g w

ith e

qual

ity

cons

train

ts o

n in

itial

stat

us p

aram

eter

s and

free

par

amet

ers o

f gro

wth

fact

ors s

peci

fic to

inte

rven

tion

(i.e.

, ste

p 3

mod

el).

2(

df) d

eriv

ed fr

om th

e co

mpa

rison

bet

wee

n st

ep 3

mod

el a

nd st

ep 4

mod

el in

whi

ch a

dditi

onal

equ

ality

con

stra

ints

wer

e ap

plie

d to

pa

ram

eter

s gro

wth

fact

ors s

peci

fic to

inte

rven

tion.

Sig

nific

ant

2(

df) i

ndic

ated

that

con

stra

inin

g pa

ram

eter

s of i

nter

vent

ion

grow

th

fact

or to

be

equa

l sig

nific

antly

wor

sene

d m

odel

fit s

uch

that

trea

tmen

t eff

ect w

as d

iffer

ent f

rom

act

ive

cont

rol e

ffec

t. a

Para

met

er w

as fi

xed

at z

ero.

†p

< .1

0. *

p<

.05.

**

p<

.01.

***

p<

.001

. Tw

o-ta

iled

test

.

148

Table 36

Cross-Classified Multilevel Mediation Model of Effect of Experimental Condition on Encounter-Specific Patient Satisfaction (PS) via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior (PCB)

Parameter Estimate 95% Bayesian CI Participant level

Intercept 3.91* [1.81, 5.90]Path Condition PCB .06 [–.08, .19]Path PCB PS .14 [–.17, .47]Path Condition PS .01 [–.06, .08]Path Baseline PCB PCB .28 [–.01, .54]Path Baseline PS PS –.07 [–.33, .20]Indirect effect .004 [–.02, .05]Residual variance PS .002* [.00, .01]Residual variance PCB .01* [.002, .03]

Patient levelPath PCB PS .33* [.29, .38]Residual variance PS .06* [.04, .08]

Within levelPath PCB PS .19* [.12, .25]Residual variance PS .07* [.05, .08]

Note. N = 992 encounters nested simultaneously within 41 participants and 714 patients. Participants who attended at least one audio were included in the analyses.

PS = patient satisfaction. PCB = patient centered behavior. Baseline PS and PCB are computed by averaging encounter-specific patient satisfaction and patient centered behavior, respectively, across encounters during the five days before intervention for each participant. Baseline PS and baseline PCB were centered at participants’ mean, respectively (i.e., grand mean centered at participant level). Condition: 1 = treatment, 0 = active control.

95% Bayesian CI = 95% Bayesian credibility interval. Bayesian estimation was used for cross-classified multilevel model and model fit statistics were not available in Mplus 7.11.

Estimates were unstandardized coefficients.

* 95% Bayesian credibility interval excluded zero.

149

Table 37

Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Agreeableness on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)

Variables PCB PSStep 1 Step 2 Step 1 Step 2

Participant levelIntercept 6.47* 6.49* 4.81* 3.80*Baseline PCB .25 .28*Baseline PS –.06 –.11Condition .06 .04 .02 .01Agreeableness .02 –.10 .01 .01Condition x Agreeableness .17*PCB .16Residual variance .02* .01* .003* .002*

Note. N = 992 encounters nested simultaneously within 41 participants and 714 patients during intervention period. Participants who attended at least one audio were included in the analyses.

PS = patient satisfaction. PCB = patient centered behavior. Baseline PS and PCB are computed by averaging encounter-specific patient satisfaction and patient centered behavior, respectively, across encounters during the five days before intervention for each participant.

Baseline PS, baseline PCB, and agreeableness were centered at participants’ mean, respectively (i.e., grand mean centered at participant level). Interaction term was computed using condition and grand-mean centered predictor. Condition: 1 = treatment, 0 = active control.

Patient satisfaction was regressed on patient centered behavior at patient- and within-level, respectively. Only participant-level results were reported.

95% Bayesian CI = 95% Bayesian credibility interval. Bayesian estimation was used forcross-classified multilevel model and model fit statistics were not available in Mplus 7.11. Estimates were unstandardized coefficients.

* 95% Bayesian credibility interval excluded zero.

150

Table 38

Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Perceived Workgroup Patient Care Climate (PWPCC) on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)

Variables PCB PSStep 1 Step 2 Step 1 Step 2

Participant levelIntercept 6.48* 6.48* 4.82* 3.99*Baseline PCB .25 .27Baseline PS –.05 –.08Condition .06 .06 .02 .01PWPCC .04 .04 .02 .01Condition x PWPCC .001PCB .13Residual variance .02* .01* .002* .002*

Note. N = 992 encounters nested simultaneously within 41 participants and 714 patients during intervention period. Participants who attended at least one audio were included in the analyses.

PS = patient satisfaction. PCB = patient centered behavior. PWPCC = perceived workgroup patient care climate. Baseline PS and PCB are computed by averaging encounter-specific patient satisfaction and patient centered behavior, respectively, across encounters during the five days before intervention for each participant.

Baseline PS, baseline PCB, and PWPCC were centered at participants’ mean, respectively (i.e., grand mean centered at participant level). Interaction term was computed using condition and grand-mean centered predictor. Condition: 1 = treatment, 0 = active control.

Patient satisfaction was regressed on patient centered behavior at patient- and within-level, respectively. Only participant-level results were reported.

95% Bayesian CI = 95% Bayesian credibility interval. Bayesian estimation was used for cross-classified multilevel model and model fit statistics were not available in Mplus 7.11. Estimates were unstandardized coefficients.

* 95% Bayesian credibility interval excluded zero.

151

Table 39

Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Daily Work Overload on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)

Variables PCB PSStep 1 Step 2 Step 1 Step 2

Participant levelIntercept 6.48* 6.47* 4.82* 3.68*Baseline PCB .26 .23Baseline PS –.07 –.11Condition .06 .07 .02 .01Mean work overload (MWO) –.02 .03 –.03 –.02Condition x MWO –.09PCB .18Residual variance .02* .01* .003* .003*

Note. N = 992 encounters nested simultaneously within 41 participants and 714 patients during intervention period. Participants who attended at least one audio were included in the analyses.

PS = patient satisfaction. PCB = patient centered behavior. MWO = mean work overload. Baseline PS and PCB are computed by averaging encounter-specific patient satisfaction and patient centered behavior, respectively, across encounters during the five days before intervention for each participant. Mean work overload was computed by averaging daily work overload from two-level data (day nested within participant) across the 10 days during intervention for each participant.

Baseline PS, baseline PCB, and mean work overload were centered at participants’ mean, respectively (i.e., grand mean centered at participant level). Interaction term was computed using condition and grand-mean centered predictor. Condition: 1 = treatment, 0= active control.

Patient satisfaction was regressed on patient centered behavior at patient- and within-level, respectively. Only participant-level results were reported.

95% Bayesian CI = 95% Bayesian credibility interval. Bayesian estimation was used for cross-classified multilevel model and model fit statistics were not available in Mplus 7.11. Estimates were unstandardized coefficients.

* 95% Bayesian credibility interval excluded zero.

152

Table 40

Cross-Classified Multilevel Moderated Mediation Model for Moderating Effect of Participants’ Agreeableness, Perceived Workgroup Patient Care Climate (PWPCC), and Daily Work Overload on Effect of Experimental Condition (via Encounter-Specific Patient-Rated Participants’ Patient Centered Behavior [PCB]) on Encounter-Specific Patient Satisfaction (PS)

Variables PCB PSStep 1 Step 2 Step 1 Step 2

Participant levelIntercept 6.49* 6.49* 4.81* 3.39*Baseline PCB .26 .20Baseline PS –.07 –.12Condition .05 .04 .02 .004Agreeableness .002 –.08 .004 .003PWPCC .04 .05 .01 –.002Mean work overload (MWO) –.001 .03 –.02 –.02Condition x Agreeableness .18†Condition x PWPCC –.11Condition x MWO –.07PCB .22Residual variance .02* .01* .003* .003*

Note. N = 992 encounters nested simultaneously within 41 participants and 714 patients during intervention period. Participants who attended at least one audio were included in the analyses. PS = patient satisfaction. PCB = patient centered behavior. PWPCC = perceived workgroup patient care climate. MWO = mean work overload. Baseline PS and PCB are computed by averaging encounter-specific patient satisfaction and patient centered behavior, respectively, across encounters during the five days before intervention for each participant. Mean work overload was computed by averaging daily work overload from two-level data (day nested within participant) across the 10 days during intervention for each participant.

Baseline PS, baseline PCB, agreeableness, PWPCC, and MWO were centered at participants’ mean, respectively (i.e., grand mean centered at participant level). Interaction term was computed using condition and grand-mean centered predictors. Condition: 1 = treatment, 0 = active control. Patient satisfaction was regressed on patient centered behavior at patient- and within-level, respectively. Only participant-level results were reported. 95% Bayesian CI = 95% Bayesian credibility interval. Bayesian estimation was used for cross-classified multilevel model and model fit statistics were not available in Mplus 7.11. Estimates were unstandardized coefficients.

* 95% Bayesian credibility interval excluded zero.

† 90% Bayesian credibility interval excluded zero.

153

Tabl

e 41

Part

icip

ant-L

evel

Pat

h-An

alyt

ic R

esul

ts o

f Ind

irec

t and

Tot

al E

ffect

s of E

xper

imen

tal C

ondi

tion

(via

Enc

ount

er-S

peci

fic P

atie

nt-R

ated

Pa

rtic

ipan

ts’ P

atie

nt C

ente

red

Beha

vior

[PC

B]) o

n En

coun

ter-

Spec

ific

Patie

nt S

atis

fact

ion

(PS)

at L

ow a

nd H

igh

Leve

ls o

f Pa

rtic

ipan

ts’ A

gree

able

ness

, Per

ceiv

ed W

orkg

roup

Pat

ient

Car

e C

limat

e, a

nd D

aily

Wor

k O

verl

oad,

resp

ectiv

ely

Var

iabl

es

PM

XP

YMD

irect

Eff

ects

(PYX

)In

dire

ct E

ffec

ts(P

YM *

PM

X)

Tota

l Eff

ects

(PYX

+ [P

YM*

PM

X])

Mod

el: A

gree

able

ness

Sim

ple

path

s for

low

agr

eeab

lene

ss–.

11.1

6.0

1–.

01–.

01Si

mpl

e pa

ths f

or h

igh

agre

eabl

enes

s.1

9*.1

6.0

1.0

3.0

4M

odel

: Per

ceiv

ed w

orkg

roup

pat

ient

car

e cl

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157

Figure 4

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Figure 5

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Figure 6

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160

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Appendix I: Recruitment Materials

Recruitment Handout

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Appendix II: Consent Forms

Employee Consent Form

University of Minnesota Worker Wellness Initiative

You are invited to be in a research study focused on patient experience and worker wellness. You were selected as a possible participant because you work for [Organization Name]. Please read this form and contact the researchers with any questions you havebefore agreeing to be in the study.

This study is being conducted by Tao Yang, Ph.D. Candidate, and Theresa Glomb,Professor, at the University of Minnesota, Carlson School of Management.

The purposes of this study are twofold: 1) to find out whether different types of wellness programs influence workers’ well-being, and 2) to collect information about patient experience.

The study is approved by the University of Minnesota Institutional Review Board (#1405P51001).

Click “Continue” to learn more about your eligibility to participate in the study.

The following employees are currently eligible for the study:

1. Full time employees (five days a week)* available during the study period between September 15 and October 3, 2014.

2. Employees who work with patients in person ** (administrative and clinical)

* Part-time or full-time 4-day work week employees will be put on a wait list and may be included in the study if space is available.

** Employees who work with patients primarily over the phone will be put on a wait list and may be included in the study if space is available.

Click “Continue” to learn more about what you would be asked to do if you chose to participate in our study.

This study has two stages – Preliminary and Active. If you agree to be in this study, you will be asked to do one thing in the Preliminary stage:

1. Complete a background survey online (15-20 minutes) that asks general questions about your job and your attitudes, behaviors, and characteristics. Once you complete the Preliminary stage, you will be eligible to enter the Active stage of the study, which will last three weeks, from September 15 to October 3, 2014, Monday through Friday. There will be no research activity on the weekend.

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During the Active stage of the study, you will be asked to do the following:

1. During the 3-week period, you will complete one short (3-5 minutes) online survey each day right before you leave the workplace. The survey is about your well-being and work experiences during the day. You will access the survey via your mobile device (e.g., smartphone, tablet, or laptop) with internet connection (if you have one) or a laptop computer provided by the research team in the workplace.

2. During the 3-week period, you will place a personal sticker (provided by the researchers) on a comment card for patients at the end of your interaction. The comment card (provided to patients upon check-in) asks about patients’ experiences during their visit. You will not be asked to collect comment cards from patients.

3. During the last 2 weeks (M - F, week 2 and 3), you will be asked to go into a designated room in the workplace each day before you start work and listen to audio guided wellness related content randomly assigned to you for 10 minutes.

a. Your employer has agreed to let you come in 15 minutes before your shift each day to listen to the audio content and this time will be considered “on the clock” and be paid for by your employer.

b. If you cannot come in early on a given day, you can listen to the audio content at the beginning of your shift and your manager has agreed to cover for you during huddle.

c. If you miss the session at the start of your shift on a given day, a makeup session will be available during the lunch hour to listen to the audio content.

Below is a table of the study activities for the three-week period.

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Overall time commitment

Your total active time commitment for the study will be approximately 4-5 hours. During the first 5 workdays, you will complete one survey a day at work of approximately 3-5 minutes and place a sticker on comment cards for each patient you have interacted with (a total of 3-4 minutes per day). During the last 10 workdays, you will add the wellness activity (10 minutes each day) in addition to the daily surveys and stickers. You will also spend 15-20 minutes to complete the background survey.

Click “Continue” to learn more about potential risks and benefits associated with participation in the study and how you can earn up to $100 for your participation.

Risks and Benefits of Being in the Study

There are only minimal risks associated with participation in this study that we want to make you aware of.

1. You might feel frustrated by having to answer one survey and place stickers on patient cards each day. For some participants, the demands of the study might become frustrating over the course of three weeks.

2. The study has the potential to interfere with your work duties. Your employer has agreed to let you do the end-of-workday surveys, sticker handoffs, and wellness activity on work time. But, if these activities interfere with your job duties, you are asked to skip the research activities and make your job the first priority.

3. Although it is only listening to wellness audio content, some participants may feel uneasy or not enjoy these activities.

4. We will be asking you to report personal information about your work attitudes and behaviors (in the background survey and daily surveys), which may feel intrusive to some participants. Since it is important to our study to have complete data, you will generally not be allowed to skip survey items except some optional items on the background survey.

If you start the study and then feel uncomfortable at any point in the process, you are free to stop your participation at any time. If you want to stop, simply stop responding to the surveys, and then let the researcher know that you are no longer going to participate in the study (email: workwell@umn.edu).

There are no proven direct benefits to participation in the study.

Compensation:

You will receive a cash card as compensation for your time and participation in this study. Payment for the three-week period is $100.00 if you have completed at least 80% of the required surveys, sticker handoffs, and wellness activities. Participants who complete the three weeks but who complete fewer than 80% of the total surveys, sticker handoffs, and wellness activities will receive $75.00. Payment will be prorated for those

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who stop the study before the end of the three-week period ($25.00 for one week, $50.00 for two weeks).

Click “Continue” to learn more about confidentiality and how to enroll in the study, or get answers to any questions you may have.

Confidentiality:

The records of this study will be kept private. In any report we might publish, we will not include any information that will make it possible to identify you. Your surveys will be associated only with a randomly assigned ID number (not your name) so that we can link the data from different sources. In reports to your organization, no individual participants will be identified and your data will be combined with those of other participants. Only the researchers will have access to your individual records, which will be identified with a randomly assigned ID number.

Voluntary Nature of the Study:

Participation in this study is voluntary. Your decision whether or not to participate will not affect your current or future relations with the University of Minnesota, or with the researchers (PhD Candidate Yang and Professor Glomb), or with [Organization Name]. If you decide to participate, you are free to withdraw at any time without affecting those relationships.

Contacts and Questions:

The researchers conducting this study are: Theresa Glomb, McFarland Professor of Organizational Behavior, and Tao Yang, Ph.D. Candidate. If you have any questions about this research, please click on the “contact researchers” button below to send an email. If you have questions later, you are encouraged to contact the researchers by email at workwell@umn.edu or 651.434.7749. If you have any questions or concerns regarding this study and would like to talk to someone other than the researcher(s), you are encouraged to contact the Research Subjects’ Advocate Line, D528 Mayo, 420 Delaware St. Southeast, Minneapolis, Minnesota 55455; (612) 625-1650.

If you would like to participate in the study, please click on the “Yes, I would like to participate” button below.

If you do not wish to participate in the study, click “No, I do not want to participate”

If you would like to ask a question, please click on the “I would like to contact the researchers with questions” button below.

[ ] Yes, I would like to participate (please click here even if you are unsure if you are eligible)

[ ] No, I do not want to participate

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[ ] I would like to contact the researchers with questions

If no,

Thank you for considering participation in the University of Minnesota Worker Wellness Initiative.

If contacting the researchers with questions,

You can contact the project researchers by writing us at workwell@umn.edu or call us at (651) 434-7749. Thank you for your interest in this study!

If yes,

Thank you for your interest in our study. Please fill in the following information so that we may contact you with information about the next step.

First Name: _____________

Last Name: _____________

Phone Number: ____________

E-mail Address: ____________

Confirm E-mail Address: ________

Please click “continue” to complete some brief screening questions, to make sure you are eligible to participate in the study.

Are you between age 18 and 65 (Yes/No)If Yes, go to next item. If No, “At this time you do not meet the requirements for participation in the study. Thank you for your interest.”

Do you work full time (five days a week)? (Yes/No)If Yes, go to next item.

If No, “Please tell us a little about your schedule. How many hours a week do you work? How many days per week do you work? Does your regular schedule include evening hours?”

Your information has been forwarded to the researchers. We are currently seeking full time employees but employees who work four days a week may also be eligible. You will be contacted by the researchers concerning your eligibility to participate in this study.

During a typical day, do you work with patients face-to-face? (Yes/No)

If Yes, “You have passed the screening criteria for the study. Please read carefully the non-disclosure statement and indicate your agreement by checking the box below.”

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To protect the random assignment study design, the researchers ask you not to share information about your wellness activity with anyone in your organization (e.g., your supervisor, coworkers, and patients) for the duration of the study, from September 15 to October 3, 2014. After completion of the study and following debriefing from the researchers, you will be free to share with others.

___ I agree with the above statement.

Thank you for agreeing to be in the study. A researcher will be contacting you soon to arrange for completion of the background survey.

If No, “Your information has been forwarded to the researchers. We are currently seeking employees who work with patients in person, but employees who work with patients by other means may also be eligible. You will be contacted by the researchers concerning your eligibility to participate in this study.”

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Patient Consent Form (English Version)

The [Organization Name] is working with researchers from the University of Minnesota on the Worker Wellness Initiative. As part of the study, we are asking patients to report on their interactions with health care professionals during their visit today by taking the attached short survey. You need to be 18 or over to participate.

How to participate?

The survey will take about 3-5 minutes to complete.You will rate the health care professionals who placed his/her sticker on the survey. Please drop the completed survey in the collection box as you exit the clinic.

You could win a $25 cash prize for participation!

Attached to this survey is a lottery ticket for a $25 cash prize drawing for the patients participating today (about 1-in-50 chance of winning). Today’s winner will be announced by 8pm the following day. Your lottery ticket (attached) has information about how to find out if you were the winner. Please keep this ticket for your record.

There are a few things that are important for you to know before you participate:

Participation is completely voluntary. If you do not wish to participate, please deposit the blank survey in the collection box. Your responses to the survey will be anonymous and strictly confidential. The researchers will not link your responses to you personally and the health care professionals will never see your responses. You are free to stop your participation at any time. If you want to stop, simply stop responding to the survey and drop it in the collection box. There are no known risks associated with participation in this study.

Would you like to participate in the survey?

Yes, I would like to participate and am 18 or older.

Great! Just complete the attached survey and place it in the collection box at the doorway. Thank you!

No, thanks. No problem. Can you help us keep track of how many surveys we distributed by dropping the blank survey in the collection box? Thank you!

Questions?

The researchers conducting this study are: Theresa Glomb, Professor, and Tao Yang, Ph.D. Candidate. If you have questions about this research, you can contact the researches at workwell@umn.edu or (651) 434-7749. If you have any questions or concerns regarding this study and would like to talk to someone other than the researcher(s), you are encouraged to contact the Research Subjects’ Advocate Line, D528 Mayo, 420 Delaware St. Southeast, Minneapolis, Minnesota 55455; (612) 625-1650.

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Patient Consent Form (Spanish Version)

La [XXX] está trabajando con investigadores de la Universidad de Minnesota en la Iniciativa para el Bienestar de los Trabajadores. Como parte del estudio, estamos solicitándole a los pacientes que nos informen acerca dela interacción que tuvieron con los profesionales del cuidado de la salud el día de hoy durante su visita, respondiendo la breve encuesta adjunta. Es necesario que usted sea mayor de 18 años para poder participar. ¿Cómo participar?

Le tomará de 3 a 5 minutos completar la encuesta.Usted evaluará a el/la profesional de la salud que haya colocado su etiqueta en la encuesta. Por favor, coloque la encuesta terminada dentro de la caja de recolección, al salir de la clínica.

¡Usted podría ganar un premio de $25 dólares por participar!Adjunto a esta encuesta se encuentra un boleto de lotería por un premio de $25 dólares, que será sorteado entre los pacientes que participen hoy (con a una probabilidad de ganar de 1 entre 50, aproximadamente). El ganador de hoy será anunciado a las 8 pm del día de mañana. Su boleto de lotería (adjunto) contiene la información para saber si usted ha resultado ganador. Por favor, conserve este boleto.

Es importante que usted sepa algunas cosas antes de participar: La participación es completamente voluntaria. Si usted no desea participar, por favor deposite la encuesta sin responder en la caja de recolección. Las respuestas que usted proporcione en la encuesta serán anónimas y estrictamente confidenciales. Los investigadores no vincularán sus respuestas con usted de manera personal, y los profesionales del cuidado de la salud nunca verán sus respuestas. Usted es libre de dejar de participar en cualquier momento. Si usted desea detenerse, simplemente deje de responder la encuesta y deposítela en la caja de recolección. No hay riesgos asociados conocidos con la participación en este estudio.

¿Le gustaría participar en esta encuesta?Sí, me gustaría participar y soy mayor de 18 años.

¡Estupendo! Solo complete la encuesta adjunta y colóquela en la caja de recolección ubicada en la puerta. ¡Gracias!

No, gracias. No hay problema. ¿Puede ayudarnos a llevar el conteo de las encuestas que distribuimos, colocando la encuesta en blanco dentro de la caja de recolección? ¡Gracias!

¿Preguntas?Los investigadores que realizan este estudio son: Theresa Glomb, Catedrática, y Tao Yang, Candidato a Doctorado. Si usted tiene preguntas o inquietudes acerca de esta investigación, puede contactar a los investigadores en workwell@umn.edu o en el número telefónico (651) 434-7749.Si usted tiene preguntas o inquietudes con respecto a este estudio y le gustaría hablar con alguien más que no sean los investigadores, le recomendamos contactar a la Línea para la Defensa de los Sujetos de Investigación, en: D528 Mayo, 420 Delaware St. Southeast, Minneapolis, Minnesota 55455; (612) 625-1650.

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Appendix III: Surveys

Employee Background Survey

Section A: About Your Work

1. Below are some descriptions of your job. How much do you agree or disagree with

these descriptions?

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

a) The job gives me a chance to use my personal initiative and judgment in carrying

out the work.

b) The job allows me to decide on my own how to go about doing my work.

c) The job gives me considerable opportunity for independence and freedom in how

I do the work.

2. Below are some ways people may feel about their jobs. Using the scale below, please

indicate how much you agree or disagree with the following statements.

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

a) Most days I am enthusiastic about my work.

b) I feel fairly satisfied with my present job.

c) I find real enjoyment in my work.

d) Each day at work seems like it will never end.

e) I consider my job rather unpleasant.

3. Please tell us about the stress you feel because of your job. Please answer candidly;

remember that all of your responses will be kept completely confidential.

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

a) I feel a great deal of stress because of my job.

b) I almost never feel stressed at work.

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4. To what extent do you agree or disagree with the following statements?

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

a) I feel emotionally drained from my work.

b) I dread having to face another day of work.

c) I feel used up from my job.

d) I feel burned out from my job.

e) I feel frustrated by my job.

5. Below are some statements regarding your skills and ability to perform your job.

Please indicate to what extent you agree or disagree with these statements.

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

At work…

a) when facing difficult tasks, I am certain that I will accomplish them.

b) I will be able to successfully overcome many challenges.

c) I am confident that I can perform effectively on many different tasks.

d) even when things are tough, I can perform quite well.

Section B: Interaction with Patients

1. These questions concern how you are able to carry out your job. Please rate what you

actually do, not what you think you should do.

How often do you. . .

1 = never, 2 = almost never, 3 = once in a while, 4 = sometimes, 5 = often, 6 = almost

always, 7 = always

a) perform the tasks that are expected as part of your job?

b) meet performance expectations?

How often do you. . .

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–9 = not applicable, 1= never, 2 = almost never, 3 = once in a while, 4 = sometimes, 5 =

often, 6 = almost always, 7 = always

a) provide quality patient care or assistance?

b) provide timely patient care or assistance?

c) spend time thinking ahead to prevent possible complications?

d) spend time planning how a patient’s status and needs might change over time?

2. How much do you agree or disagree with the following statements about your

interactions with patients?

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

During interactions with patients, …

a) I frequently try to take patients’ perspectives.

b) I ask questions that show my understanding of patients’ situations.

c) I listen carefully to patients.

d) I respond to patients after careful considerations.

e) I make an effort to see the world through patients’ eyes.

f) I regularly seek to understand patients’ viewpoints.

g) I find my mind wandering.

h) I show patients that I am listening by using verbal acknowledgements or body

language (e.g., head nods).

i) I am sensitive to patients’ subtle meanings or cues.

j) I think of more than one way to react to patients.

k) I carefully observe how patients respond.

l) I pause to consider multiple options before responding to patients.

m) I react to patients in ways based on their situations.

n) I often imagine how patients are feeling.

3. On average, how many patients do you see or assist in a typical workday?

__________

4. In the last three months, how many medical errors did you report? ___

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Section C: Your Work Environment

1. How would you rate the following statements regarding patient care and assistance in

your department? Think of your department as the one in which you work, not as the

[Organization Name] as a whole. If you are affiliated with more than one department,

think of the department where you report most frequently to your supervisor. Please rate

the following on the basis of your personal observation and experience.

1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent

How would you rate. . .

a) the job knowledge and skills of employees in your department to deliver superior

quality patient care and assistance?

b) efforts to measure and track the quality of patient care and assistance in your

department?

c) the recognition and rewards employees receive for the delivery of superior patient

care and assistance in your department?

d) the overall quality of patient care and assistance provided by your department?

e) the leadership shown by management in your department in supporting the patient

care and assistance quality effort?

f) the effectiveness of your department’s communications efforts to both employees

and patients?

g) the tools, technology, and other resources provided by your department to

employees to support the delivery of superior quality patient care and assistance?

2. Below are statements about your immediate supervisor. To what extent do you agree or

disagree with each statement?

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

a) My supervisor takes pride in my accomplishments.

b) My supervisor really cares about my well-being.

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c) My supervisor values my contributions.

d) My supervisor strongly considers my goals and values.

e) My supervisor shows little concern for me.

f) My supervisor is willing to help me if I need a special favor.

3. Think about the coworkers who are closest to you at work. Below you will find a list of

things that your closest coworkers may have done in recent weeks. Please read each item

carefully and indicate how often these things have happened in your life in the previous

month.

1 = never, 2 = once or twice, 3 = about once a week, 4 = several times a week, 5 = almost

every day, 6 = every day

How often have the coworkers closest to you . . .

a) let you know that you did something well?

b) told you what s/he did in a situation that was similar to yours?

c) pitched in to help you do something that needed to be done?

d) expressed respect for a personal characteristic/quality of yours?

e) listened to your concerns about work?

f) helped you handle a stressful day at work?

Section D: About Yourself

1. Below are some ways people describe themselves. Using the scale below, please

indicate to what extent you agree or disagree with these statements about you. Think

about yourself as you generally are, not as you wish to be or think you should be.

1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5

= slightly agree, 6 = agree, 7 = strongly agree

I see myself as someone who. . .

a) is the life of the party.

b) sympathizes with others’ feelings.

c) gets chores done right away.

d) has frequent mood swings.

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e) has a vivid imagination.

f) doesn’t talk a lot.

g) is not interested in other people's problems.

h) often forgets to put things back in their proper place.

i) is relaxed most of the time.

j) is not interested in abstract ideas.

k) talks to a lot of different people at parties.

l) feels others’ emotions.

m) likes order.

n) gets upset easily.

o) has difficulty understanding abstract ideas.

p) keeps in the background.

q) is not really interested in others.

r) makes a mess of things.

s) seldom feels blue.

t) does not have a good imagination.

2. Below is a collection of statements about your everyday experience. Using the scale

below, please indicate how frequently or infrequently you generally have each

experience. Please answer according to what really reflects your experience rather than

what you think your experience should be.

6 = almost always, 5 = very frequently, 4 = somewhat frequently, 3 = somewhat

infrequently, 2 = very infrequently, 1 = almost never

a) I could be experiencing some emotion and not be conscious of it until some time

later.

b) I break or spill things because of carelessness, not paying attention, or thinking of

something else.

c) I find it difficult to stay focused on what’s happening in the present.

d) I tend to walk quickly to get where I’m going without paying attention to what I

experience along the way.

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e) I tend not to notice feelings of physical tension or discomfort until they really

grab my attention.

f) I forget a person’s name almost as soon as I’ve been told it for the first time.

g) It seems I am “running on automatic” without much awareness of what I’m doing.

h) I rush through activities without being really attentive to them.

i) I get so focused on the goal I want to achieve that I lose touch with what I am

doing right now to get there.

j) I do jobs or tasks automatically, without being aware of what I’m doing.

k) I find myself listening to someone with one ear, doing something else at the same

time.

l) I drive places on “automatic pilot” and then wonder why I went there.

m) I find myself preoccupied with the future or the past.

n) I find myself doing things without paying attention.

o) I snack without being aware that I’m eating.

3. Over the last three months, would you say your health is ...

Poor

Fair

Good

Very Good

Excellent

4. Over the past three months, how often have you . . .

1 = never, 2 = occasionally, 3 = sometimes, 4 = fairly often, 5 = most of the time

a) generally enjoyed the things you do?

b) felt tense or high-strung?

c) felt calm and peaceful?

d) felt downhearted and blue?

e) felt restless, fidgety, or impatient?

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f) been moody or brooded about things?

g) felt cheerful and lighthearted?

h) been rattled, upset, or frustrated?

i) been a happy person?

j) been in low or very low spirits?

k) been anxious or worried?

l) felt tired?

m) felt depressed?

n) felt sluggish?

5. Are you . . . (select one)

[ ] male? [ ] female?[ ] Choose not to answer

6. How old are you? (in years) ________

7. What is the highest level of education you have completed? (select one)

Some high school (grade 11 or less) Graduated from high school or G.E.D. Some college or technical training beyond high school (1-3 years) Graduated from college (B.A., B.S., or other Bachelor’s degree) Some graduate school Graduate or professional degree (MBA, Master’s, Ph.D., J.D., etc.)

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8. Are you . . . (check all that apply)

Asian, Asian American, or Pacific Islander? Black, African, or African American? Hispanic, Chicano, Latino, or Hispanic American? Middle Eastern, Arab, or Arab American? Native American, American Indian, or Alaskan Native? White, European, or European American? Other? Choose not to answer

9. What is your current marital status?

Single Married Same-sex domestic partnerLiving with a significant other or partner Divorced or separated Widowed Choose not to answer

10. How many hours do you work in a typical week? __________

11. When did you first come to work for your organization? ___ (month)/___ (year)

12. When did you start your current job in this organization? ___ (month)/___ (year)

13. What is your official job title? ___________

14. In which team/department at the [Organization Name] do you work? ___________

15. In your current position, do you supervise or manage other employees?

Yes

No

16. To best accommodate your work schedule during the Active Phase of the study,

please indicate your work schedule during the three-week period from September 15

(Monday of week1) to October 3, 2014 (Friday of week 3). We will arrange daily

research activities around your schedule.

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From September 15 (Monday of week 1) to October 3, 2014 (Friday of week 3). . .

a) when will you normally start work? ____ am / pm (circle one)

b) when will your work normally end? ____ am / pm (circle one)

17. To access daily short surveys before leaving the workplace, do you prefer to use…?

[ ] personal device with internet connection (e.g., smartphone, tablet, laptop)?

(Recommended)

[ ] laptop provided by researchers at the worksite?

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Employee End-of-Workday Survey

The first set of questions ask about your experiences yesterday. Tell us about your sleep, physical activity, and meals from yesterday.

How well did you sleep last night?

Very Poorly Poorly Moderately Well Well Very Well

How physically active (walking, biking, exercising) were you yesterday (during the day and evening)?

Not At All Slightly Moderately Quite A Bit Very Much

How nutritious and healthy were your meals and snacks yesterday (during the day and evening)?

Not At All Slightly Moderately Quite A Bit Very Much

The next set of questions ask about your experience today during your workday.

Today, which shift did you work?

I did not work today. I worked morning shift only. I worked afternoon shift only. I worked both morning and afternoon shifts.

Today, how many hours did you work? _______ (choose from 1, 2 …16, 17 or more)

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Today at work, approximately how many patients did you see or assist? (choose from 0, 1, … 100, 101 or more)

Today at work, did you have to work faster than you would like to complete your work?

Not At All Slightly Somewhat Quite A Bit Very Much

Today at work, did you have too much work to do in too little time?

Not At All Slightly Somewhat Quite A Bit Very Much

Today at work, how much pressure did you feel under?

Not At All Slightly Somewhat Quite A Bit Very Much

Today at work, did you feel like you decided for yourself what to do?

Not At All To A Very Little Extent To Some Extent To A Large Extent To A Very Large Extent

Today at work, I found myself doing things without paying attention.

Not At All Slightly Somewhat Quite A Bit Very Much

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Today at work, I did jobs or tasks automatically, without being aware of what I was doing.

Not At All Slightly Somewhat Quite A Bit Very Much

Today at work, I rushed through activities without being really attentive to them.

Not At All Slightly Somewhat Quite A Bit Very Much

Today at work, the quality of care or assistance I provided has been

Far below my personal average Below my personal average About my personal average Above my personal average Far above my personal average

Today at work, the timeliness of care or assistance I provided has been

Far below my personal average Below my personal average About my personal average Above my personal average Far above my personal average

Today at work, my overall performance has been

Far below my personal average Below my personal average About my personal average Above my personal average Far above my personal average

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Today at work, how often did you spend time planning how a patient’s status and needs might change over time?

Not Applicable Never Rarely Sometimes Often Always

Today at work, I made an effort to see the world through patients’ eyes.

Never Rarely Sometimes Often Always

Today at work, I paused to consider multiple options before responding to patients.

Never Rarely Sometimes Often Always

Today at work, I tried to take patients’ perspectives.

Never Rarely Sometimes Often Always

Today at work, I listened carefully to patients.

Never Rarely Sometimes Often Always

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Today at work, I was sensitive to patients’ subtle meanings or cues.

Never Rarely Sometimes Often Always

Today at work, I thought of more than one way to react to patients.

Never Rarely Sometimes Often Always

Today at work, how stressed did you feel?

Not At All Slightly Moderately Quite A Bit Very Much

Today at work, I was in a good mood.

Not At All Slightly Moderately Quite A Bit Very Much

Today, I was satisfied with my job.

Not At All Slightly Moderately Quite A Bit Very Much

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Today, I felt emotionally drained from my work.

Strongly Disagree Disagree Neither Agree nor Disagree Agree Strongly Agree

Right now, do you feel tired?

Not At All To A Very Little Extent To Some Extent To A Large Extent To A Very Large Extent

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Patient Survey (English Version)

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214

Patient Survey (Spanish Version)

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Appendix IV: Active Phase Materials Active Phase Guide

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Audio Player Manual

How to Use the Audio Player?Connect the earbuds to the player. Press and hold the Play/Pause button to power on the player. The audio should play automatically. If the audio does not play, press the Play/Pause button again. The audio should automatically start from the beginning. If not, press the backward key to restart. The player carries one (different) audio session each day. Press the volume keys to adjust the volume. The audio lasts 10 minutes. Once you have reached the end of the audio, press and hold the Play/Pause button to power off the player.

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Appendix V: Debriefing Materials

Employee Debriefing Questionnaire

Thank you for your time and participation in the Worker Wellness Initiative! We would like some information about your reactions to the activities that you completed in the study. Please choose the circle that best describes your reactions to the study.

To what extent did you enjoy being a participant in the study?

Not at all A little A moderate amount A lot

To what extent did your study activities interfere with your carrying out work activities?

Not at allA little A moderate amount A lot

To what extent did your study activities interfere with your personal/family activities?

Not at all A little A moderate amount A lot

To what extent were you aware of the wellness audio content for your coworkers?

Not at all A little A moderate amount A lot

How supportive did you think your organization was for your participation in the study?

Not at all A little A moderate amount A great deal

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Did you ever miss an audio session?

No, I don’t recall missing a session. Yes, I missed a session.

If No, I don’t recall missing ... Is Selected, Then Skip To Did you ever miss attaching a stick...

I missed an audio session because . . . (please check all that apply)

I did not work that day. I worked off site that day. I was on leave for sickness or emergency. I was too busy working. I needed to attend to my personal/family activities. I thought the audio was not helpful for me. I felt the audio was too boring. Other (please write in below)

If "Other", please write in specifics.

Did you ever miss attaching a sticker or handing off a patient survey?

Not applicable; I do not work with patients face-to-face. No, I don’t recall missing a sticker or survey handoff. Yes, I missed a sticker or survey handoff.

If Yes, I missed a sticker or ... Is Not Selected, Then Skip To Did you have any experience in meditation...

I missed a sticker or survey handoff because. . . (please check all that apply)

I knew the patient had taken the survey before. I was too busy working. The patient declined the sticker or survey (e.g., low literacy to read, difficulty checking lottery number online). Other (please write in below)

If "Other", please write in specifics.

Did you have any experience in meditation prior to the study?

No Yes

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If Yes, please tell us a bit more about your meditation experience (e.g., what kind of meditation, when, where, and for how long) ___________

We may be interested in following up with you in the future to learn about any potential long-term influence of the Worker Wellness Initiative. Would it be all right if the researchers followed up with you in the future?

No Yes

If you would like, use the space below to give us feedback about your experiences in the study.

_________________

In about 10 days, we will mail you a notice of your completion rate, your compensation for participation (cash card with the amount according to your completion rate), and information about the wellness audio content and additional resources. Please fill out your name and valid mailing address.

First Name ________

Last Name ________

Stress Address ________

City ________

State ________

Zip ________

Email ________

Thank you for your time and participation in the Worker Wellness Initiative!

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Employee Debriefing Handout

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Appendix VI: Supplemental Results of Latent Growth Modeling using Five Waves of Repeated Measures

Mindfulness

As shown in Table 23, linear growth model using five waves of repeated

measures yielded good fit in the treatment and active control groups separately

2 2 (16) = 32.03,

RMSEA = .18, CFI = .87). As shown in Table 24, growth factor mean was positive and

close to significance in the treatment condition (mean slope = 0.03, p = .10) and was

nonsignificant in the active control condition (mean slope = 0.02, p > .10). In the two-

group latent growth modeling with equality constraints on initial status parameters across

conditi 2 (36) = 56.51, RMSEA

= .13, CFI = .92, treatment growth factor mean was positive and marginally significant

(mean slope = .03, p < .10) whereas active control growth factor mean was not significant

(mean slope = .02, p > .10), as shown in Table 25. Results suggested that treatment led to

a 0.03-unit increase of mindfulness each wave (i.e., every two days) over intervention

period after taking into account normative development, whereas active control did not

produce change in mindfulness over time. The two-group analysis with additional

2 (39) = 57.58,

2 (3) = 1.07, p > .10.

Thus, the treatment effect was not different from active control effect. As robustness

check, I reran the above analyses using mindfulness on Day 1, 3, 5, 7, and 9 and Day 2, 4,

6, 8, and 10 during intervention, respectively. The results were consistent with those from

the 5-wave analyses; the treatment effect was not different from the active control effect.

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Job satisfaction

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (16) = 19.03,

RMSEA = .08, CFI = .95). As shown in Table 24, growth factor means were not

significant in the treatment or active control condition (mean slope = .03 and –.02,

respectively, ps > .10). In the two-group latent growth modeling with equality constraints

on initial status parameters across conditions and free parameters of intervention growth

2 (34) = 36.16, RMSEA = .05, CFI = .99, treatment and active control growth

factor means were not significant (mean slope = .03 and –.01, respectively, ps > .10), as

shown in Table 26. Results suggested that neither treatment nor active control produced

change in job satisfaction over time. The two-group analysis with additional equality

2 (37) = 38.79, RMSEA

2 (3) = 2.63, p > .10. Thus, the

treatment effect was not different from active control effect.

Job performance

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (16) = 26.69,

RMSEA = .14, CFI = .89). As shown in Table 24, growth factor mean was positive and

significant in the treatment condition (mean slope = .07, p < .01) but was not significant

in the active control condition (mean slope = .01, p > .10). In the two-group latent growth

modeling with equality constraints on initial status parameters across conditions and free

2 (34) = 46.68, RMSEA = .11, CFI = .95,

225

treatment growth factor mean was positive and significant (mean slope = .07, p < .01)

whereas active control growth factor mean was not significant (mean slope = .004,

p > .10), as shown in Table 27. Results suggested that treatment led to 0.07-unit increase

of job performance each wave (i.e., every two days) while active control produced no

change in job performance over time. The two-group analysis with additional equality

2 (37) = 51.95, RMSEA

2 (3) = 5.27, p = .15. Thus, the

treatment effect was not different from active control effect.

Proactive patient care

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (18) = 29.84,

RMSEA = .15, CFI = .91). As shown in Table 24, growth factor mean was not significant

in either the treatment or active control condition (mean slope = .03 and –.01, p > .10). In

the two-group latent growth modeling with equality constraints on initial status

parameters across conditions and free parameters of intervention growth factors, 2 (36) =

53.99, RMSEA = .13, CFI = .93, treatment and active control growth factor means were

not significant (mean slope = .03 and –.01, ps > .10), as shown in Table 28. Results

suggested that neither treatment nor active control led to change in proactive patient care

over time. The two-group analysis with additional equality constraints on parameters of

2 (37) = 60.37, RMSEA = .15, CFI = .91) significantly

2 (1) = 6.38, p < .05. Thus, the treatment effect was different from

active control effect.

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Participant-rated patient centered behavior

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

(tr 2 2 (16) = 17.74,

RMSEA = .06, CFI = .99). As shown in Table 24, growth factor mean was positive and

significant in the treatment condition (mean slope = .06, p < .01) but was not significant

in the active control condition (mean slope = .03, p > .10). In the two-group latent growth

modeling with equality constraints on initial status parameters across conditions and free

2 (34) = 41.83, RMSEA = .09, CFI = .98,

treatment growth factor mean was positive and significant (mean slope = .05, p < .01)

while active control growth factor mean was not significant, as shown in Table 29. The

two-group analysis with additional equality constraints on parameters of growth factors

2 (37) = 43.91, RMSEA = .08, CFI = .99) did not significantly worsen

2 (3) = 2.08, p > .10. Thus, the treatment effect was not different from active

control effect.

Stress

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (16) = 28.01,

RMSEA = .15, CFI = .76). As shown in Table 24, growth factor mean was not significant

in the treatment or active control condition (mean slope = –.03 and –.004, p > .10). In the

two-group latent growth modeling with equality constraints on initial status parameters

across conditions and free param 2 (34) = 44.91,

RMSEA = .10, CFI = .91, treatment and active control growth factor means were not

227

significant (mean slope = –.03 and < .001, ps > .10), as shown in Table 30. The two-

group analysis with additional equality constraints on parameters of growth factors across

2 (37) = 47.18, RMSEA = .09, CFI = .92) did not significantly worsen model

2 (3) = 2.27, p > .10. Thus, the treatment effect was not different from active control

effect.

Emotional exhaustion

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (18) = 20.99,

RMSEA = .07, CFI = .96). As shown in Table 24, growth factor mean was not significant

in the treatment or active control condition (mean slope = –.04 and .02, ps > .10). In the

two-group latent growth modeling with equality constraints on initial status parameters

2 (36) =36.95,

RMSEA = .03, CFI = .99, treatment and active control growth factor means were not

significant (mean slope = –.04 and .02, ps > .10), as shown in Table 31. The two-group

analysis with additional equality constraints on parameters of growth factors across

2 (37) = 43.33, RMSEA = .07, CFI = .95) significantly worsened model fit,

2 (1) = 6.38, p < .05. Thus, the treatment effect was different from active control effect.

Fatigue

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment and active control groups separately

2 2 (16) = 23.86,

RMSEA = .12, CFI = .86). As shown in Table 24, growth factor mean was negative and

significant in the treatment condition (mean slope = –.09, p < .01) but was not significant

228

in the active control condition (mean slope = –.04, p > .10). In the two-group latent

growth modeling with equality constraints on initial status parameters across conditions

2 (34) =48.65, RMSEA = .12, CFI

= .86, treatment led to a 0.09-unit decrease of fatigue each wave (i.e., every two days)

whereas active control did not produce change in fatigue, as shown in Table 32. The two-

group analysis with additional equality constraints on parameters of growth factors across

2 (37) = 50.28, RMSEA = .11, CFI = .87) did not significantly worsen model

2 (3) = 1.63, p > .10. Thus, the treatment effect was not different from active control

effect.

Patient-rated patient centered behavior

As shown in Table 23, linear growth model using five waves of repeated

measures yielded very good fit in the treatment group and modest fit in the active control

2 (16) = 14.75, RMSEA = .00, CFI = 1.00; active control:

2 (18) = 33.43, RMSEA = .19, CFI = .54). As shown in Table 24, growth factor means

were not significant in the treatment and active control conditions (mean slope = .001

and .01, ps > .10). In the two-group latent growth modeling with equality constraints on

initial status parameters across conditions and free parameters of intervention growth

2 (36) = 49.49, RMSEA = .12, CFI = .64, neither treatment nor active control

produced change in patient rated patient centered behavior over time, as shown in Table

33. The two-group analysis with additional equality constraints on parameters of growth

2 (39) = 50.32, RMSEA = .11, CFI = .70) did not significantly

2 (3) = 0.83, p > .10. Thus, the treatment effect was not different

from active control effect.

229

Patient satisfaction

As shown in Table 23, linear growth model using five waves of repeated

measures yielded modest fit in the treatment and active control groups separately

2 2 (18) = 22.11,

RMSEA = .10, CFI = .00). As shown in Table 24, growth factor mean was positive and

significant in the treatment condition (mean slope = .02, p < .05) but was not significant

in the active control condition (mean slope = .01, p > .10). In the two-group latent growth

modeling with equality constraints on initial status parameters across conditions and free

2 (37) = 45.75, RMSEA = .10, CFI = .08,

treatment led to a 0.02-unit of increase in patient satisfaction each wave (i.e., every two

days) whereas active control did not produce change in patient satisfaction over time, as

shown in Table 34. The two-group analysis with additional equality constraints on

2 (39) = 49.22, RMSEA = .10, CFI

= .00 2 (2) = 3.47, p > .10. Thus, the treatment

effect was not different from active control effect.