Mental Disorder Profiles in Justice-Involved Youth

by

Shiming Huang

A thesis submitted in conformity with the requirements

for the degree of Master of Arts

Department of Applied Psychology & Human Development

Ontario Institute for Studies in Education

University of Toronto

© Copyright by Shiming Huang (2016)

ii

Mental Disorder Profiles in Justice-Involved Youth

Shiming Huang

Master of Arts

Department of Applied Psychology & Human Development

University of Toronto

2016

Abstract

As a population, justice-involved youth are characterized by high rates of comorbid mental

disorders. However, past research has generally considered disorders singly. It is unclear whether

disorders cluster together in a way that has implications for intervention and case management of

justice-involved youth. To begin to explore this issue, cluster analysis was used to identify

common diagnostic profiles in a sample of 195 youth who received court-ordered assessments.

Six mental disorder clusters emerged, which were then examined in relation to youths’ risk to

reoffend, criminogenic needs, interventions received during probation, and recidivism. Two

clusters were characterized by relatively higher reoffense rates: youth with disruptive behavior

disorders alone, and youth with the profile of disruptive behavior disorders, learning disability,

and ADHD. These clusters also significantly predicted odds of reoffense even after accounting

for youths’ static risk and criminogenic needs treatment. Implications for risk assessment and

rehabilitation of justice-involved youth are discussed.

iii

Acknowledgments

Thank you to the youth justice lab for the constant support and encouragement. I am fortunate to

be working in a wonderfully loving and passionate team. Thank you to Michele, for your

unwavering support and guidance throughout this journey. I look forward to learning from you

as you remain a constant source of inspiration for me. Thank you to Tracey, for your insight and

supervision, to teach me to think more conceptually and grow as a researcher. Lastly, thank you

to my SCCP colleagues, for your friendship and inspiration.

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Table of Contents

Abstract ........................................................................................................................................... ii

List of Tables ...................................................................................................................................v

Introduction ......................................................................................................................................1

Mental disorders in justice-involved youth .................................................................................1

Mental health and offending .......................................................................................................2

The present study ........................................................................................................................5

Method .............................................................................................................................................6

Participants ..................................................................................................................................6

Procedure.....................................................................................................................................7

Measures .....................................................................................................................................8

Mental disorder diagnoses .....................................................................................................8

Risk to reoffend and criminogenic needs ..............................................................................8

Matching of identified criminogenic needs ...........................................................................9

Recidivism ...........................................................................................................................10

Statistical analyses ....................................................................................................................10

Mental disorder profiles .......................................................................................................10

Treatment match across clusters ..........................................................................................10

Predicting recidivism ...........................................................................................................10

Results ............................................................................................................................................11

Preliminary analyses .................................................................................................................11

Mental illness profiles ...............................................................................................................13

Are identified criminogenic needs being met at a similar rate across the mental disorder

clusters?..............................................................................................................................14

Does matching services to identified criminogenic needs predict recidivism equally well

across the mental disorder clusters? ...................................................................................15

Discussion ......................................................................................................................................22

Mental disorder profiles ............................................................................................................22

Matching services to criminogenic needs .................................................................................25

Mental disorder profiles and recidivism ...................................................................................27

Implications for risk, need, and responsivity ............................................................................29

Strengths and limitations ...........................................................................................................30

References ......................................................................................................................................32

v

List of Tables

Table 1: Participants’ demographic, offense, and psychiatric information ....................................2

Table 2: Percentage treatment match scores within each criminogenic need and recidivism

rates across mental disorder clusters .......................................................................................15

Table 3: Hierarchical regression analyses predicting recidivism from criminal history, match,

and mental disorder profiles .....................................................................................................19

Table 4: Disruptive behavior disorders related YLS/CMI items by mental disorder clusters .......21

1

INTRODUCTION

Youth with mental illnesses are significantly overrepresented in the criminal justice system

(Teplin et al., 2007). Research results suggest that more than 65% of justice-system-involved

youth have at least one psychiatric disorder (e.g., Shufelt & Cocozza, 2006; Teplin et al., 2007;

Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002), far exceeding rates found in the

general population (Waddell, Shepherd, Schwartz, & Barican, 2014). In addition, comorbidity

(i.e. the co-occurrence or two or more mental disorders) is highly prevalent in this population,

with 58% to 79% of justice-involved youth meeting diagnostic criteria for two or more mental

disorders (Abram, Teplin, McClelland, & Dulcan, 2003; Abrantes, Hoffmann, Anton, & Estroff,

2004; Shufelt & Cocozza, 2006; Ulzen & Hamilton, 1998). Justice-involved youth with

comorbid psychiatric conditions are a particularly vulnerable group, as individuals with multiple

diagnoses often exhibit poorer psychosocial outcomes compared to individuals with single

diagnoses (Drake, Mueser, Clark, & Wallach, 1996; Kessler et al., 1994). Thus, an increased

understanding of the relationship of comorbidity patterns to justice-involved youth’s outcomes is

needed. This entails elucidating the interrelationships among mental health variables, criminal

risk factors, intervention, and recidivism outcomes.

Mental disorders in justice-involved youth

Despite the high prevalence rates of comorbid mental disorders in justice-involved youth,

past research has focused mainly on single diagnoses and their influence on youth’s

rehabilitation and other life outcomes. Consistently across studies, disruptive behavior disorders

(DBD), such as conduct disorder (CD) and oppositional defiant disorder (ODD), have emerged

as the most prevalent mental disorders in justice involved youth (Fazel, Doll, & Langstrom, 2008;

Teplin, Abram, McClelland, Dulcan, & Mericle, 2002). Implications for justice-involved youth

2

with disruptive behavior disorders include increased likelihood of persistent adult dysfunction

(Zoccolillo, Pickles, Quinton, & Rutter, 1992) and continued engagement in antisocial behaviors

in adulthood (Loeber, Burke, Lahey, Winters, & Zera, 2000). Other common psychopathologies

in this population include substance use disorders (SUD), affective disorders, learning

disabilities (LD), and ADHD (Brink, 2005; Fazel et al., 2008; Murphy, 1986; Teplin et al., 2002).

The outcomes of these mental disorders in justice-involved youth have also been investigated.

For example, adolescents with learning disabilities display higher levels of antisocial behaviors

and arrest rates than non-learning-disabled peers (Keilitz & Dunivant, 1986; Waldie & Spreen,

1993), and justice-involved youth with ADHD were more likely to recidivate compared to non-

ADHD youth (Gordon, Diehl, & Anderson, 2012).

In contrast to the number of studies examining the prevalence rates of psychiatric

comorbidities in justice-involved youth (Abram et al., 2003; Abrantes et al., 2004), limited

research has examined the significance of such comorbidity patterns on outcomes in this

population. Some of the most common comorbidity patterns found in community, clinical, and

forensic studies include the co-occurrence of disruptive behavior disorders with ADHD,

disruptive behavior disorders with SUD, and SUD with ADHD (Abram et al., 2003; Armstrong

& Costelo, 2002; Chan, Dennis, & Funk, 2008; Milin, Halikas, Meller, & Morse, 1991;

Vermeiren, 2003). Despite their high prevalence, the specific relationships between these

comorbidity patterns and outcomes for justice-involved youth have yet to be elucidated.

Mental health and offending

The role of mental illness in criminal behavior and justice system involvement has been

examined in the literature, with research producing mixed results. Some studies have reported

that untreated mental disorders are risk factors for criminal justice system involvement and

recidivism (Hoeve, McReynolds, & Wasserman, 2013; Vermeiren, Schwab-Stone, Ruchkin, De

3

Clippele, & Deboutte, 2002) while others have found no meaningful correlations between mental

illness and antisocial behaviors (Skeem, Manchak, & Peterson, 2011). However, most of the

existing studies on mental health and offending have focused on single diagnoses and few have

examined links between particular patterns of mental disorders and offending. Within this small

literature, there is evidence that the co-occurrence of particular mental disorders is associated

with antisocial behaviors and recidivism outcomes. For example, some research shows that

individuals with substance abuse disorders alongside other psychopathologies are at increased

risk for recidivism compared to those with substance use disorders alone (Rezansoff,

Moniruzzaman, Gress, & Somers, 2013; Wallace, Mullen, & Burgess, 2004). The co-occurrence

of ADHD and CD is associated with earlier age of onset of antisocial behaviors (Loeber et al.,

2000), and greater variety and severity of antisocial behaviors than CD alone (Walker, Lahey,

Hynd, & Frame, 1987). However, differing definitions of mental disorders and differences in

which disorders are included in studies, as well as varied samples, hinder our understanding of

the relationship of comorbidities to antisocial outcomes.

Furthermore, few studies have examined justice-involved youth with mental disorders in

the context of the risk-need-responsivity framework, an assessment and case management

framework that is widely used to identify targets of intervention to reduce reoffending in justice

system-involved youth and adults (Andrews & Bonta, 2010; Andrews, Bonta, & Hoge, 1990).

Three principles form the core of the RNR framework. The risk principle states that the intensity

of service should match an offender’s level of risk level for recidivism. The need principle states

that each offender’s criminogenic needs – defined as risk factors that are strongly and directly

related to criminal behavior – should be the targets of rehabilitative interventions. Criminogenic

needs that have been well-validated in empirical studies include: history of antisocial behavior,

antisocial personality pattern, antisocial cognition/attitudes, antisocial peers, family dysfunction,

4

school/work obstacles, misguided use of leisure time, and substance abuse (Andrews & Bonta,

2010). With the exception of history of antisocial behavior, a static risk factor, criminogenic

needs represent dynamic risk factors in that they are amenable to change and therefore constitute

targets of intervention. Substantial research supports the utility of targeting criminogenic needs

in reducing reoffending (Andrews & Bonta, 2010; Andrews et al., 1990; Vieira, Skilling, &

Peterson-Badali, 2009). Finally, the responsivity principle states that treatment should entail

cognitive social learning interventions that are tailored to an individual’s unique characteristics

(Andrews et al. 1990; Bonta & Andrews, 2006).

In contrast to the dominance of the RNR framework in understanding and addressing risk

factors for continued offending and in the rehabilitation of justice-involved individuals, there is a

paucity of research examining mental health within this framework. Most research has focused

on the adult justice population and conceptualized mental health as singular disorders as defined

within the DSM-IV-TR (American Psychiatric Association, 2000). In studies where mental health

was defined as singular disorders and investigated within the context of the RNR framework,

criminogenic needs predicted recidivism equally well in individuals with and without mental

disorders and no incremental validity was added by the inclusion of clinical variables (e.g.,

mental disorder diagnoses, treatment history) to the prediction of reoffending (Bonta, Law, &

Hanson, 1998; Skeem, Winter, Kennealy, Louden, & Tatar, 2014). To date, only a handful of

studies have attempted to examine this issue in the youth justice population (McCormick,

Peterson-Badali, & Skilling, 2016; Shubert, Mulvey & Glasheen, 2011), and even fewer have

conceptualized mental health in terms of comorbid mental disorders or clusters rather than

discrete, singular diagnoses (Guebert & Olver, 2014). The few studies available relevant to

examining the links between mental health, criminogenic needs, and recidivism in justice-

involved youth has produced conflicting results. Some studies reported that mental health status

5

was unrelated to recidivism (McCormick et al., 2016) while others found that specific mental

disorder patterns, such as CD and substance abuse, were related to more serious criminogenic

need profiles and to higher rates of reoffending (Guebert & Olver, 2014). Given such

inconsistencies in the literature, further research regarding the relationships between mental

health variables, criminogenic needs, and recidivism outcomes among justice-involved youth is

warranted.

The present study

The existing literature has primarily investigated mental disorders as single diagnoses.

However, given the common occurrence of psychiatric comorbidities, conceptualizing mental

health solely as singular diagnoses may not be representative of the realities of the justice

populations. The high prevalence rates and the consistency of certain comorbidity profiles in the

justice populations may represent meaningful clusters that differentiate between subsets of

justice-involved individuals and have implications for treatment and/or recidivism outcomes.

Thus, the current study attempted to identify common mental disorder profiles in a sample of

justice-involved youth, and understand their relationships to criminogenic needs, treatments

received during probation, and recidivism outcomes.

Three research questions were examined: (1) What are the common mental disorder

profiles in justice-involved youth? It was expected that common profiles observed in community

and clinical studies would also be found in a sample of justice-involved youth. For example, we

anticipated the co-occurrence of disruptive behavior disorders with ADHD (Vermeiren, 2003),

and substance use/abuse disorders with disruptive behavior disorders (Armstrong & Castello,

2002; Chan et al., 2008). (2) Are criminogenic needs being addressed at similar rates across the

mental disorder profiles? The RNR framework stipulates that interventions should target

individuals’ identified criminogenic needs to reduce risk of reoffending (Andrews et al. 1990;

6

Bonta & Andrews, 2006). Thus, it is important to establish whether differences exist across the

mental disorders in the rate of addressing individuals’ identified criminogenic needs. (3) Does

addressing youths’ identified criminogenic needs via case management and treatment services

have an impact on re-offense rates across the mental disorder profiles? RNR principles and

previous research indicate that case management targeting individuals’ identified criminogenic

needs is associated with reduced reoffending (Peterson-Badali, Skilling, & Haqanee, 2015;

Vieira, Skilling, & Peterson-Badali, 2009). It was expected that this finding would also hold in

youth regardless of their specific mental disorder profile.

To answer the above research questions, information including mental disorder diagnoses,

services received during probation, and recidivism data were collected from a sample of

community-sentenced youth who received comprehensive assessments that included information

related to mental health, criminogenic needs, and risk to reoffend.

METHOD

Participants

The sample consisted of 195 youth (154 males and 41 females), ranging from 12 to 20

years of age (M = 16 years, SD = 1.56), referred for court-ordered assessments between 2003 and

2010 to a mental health agency in Toronto, Canada in order to assist the court in disposition

decisions; assessments were conducted by members of a multidisciplinary team including a

psychiatrist and a psychologist. Youth in this study sample met diagnostic criteria for at least one

or more mental disorders. Participants were included through consecutive admission and consent

was obtained from all current study participants for their clinical information to be used for

research purposes. The overall research consent rate in the clinic was 79%.

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As shown in table 1, 32% of the participants reported their ethnicity as Black, 27% as

White, and 7% as Asian. The offenses leading to participants’ referrals for assessment included

non-violent (e.g., theft, drug-related, break and enter), violent but not sexual (e.g., assault,

robbery), and sexual offenses (e.g., sexual assault, invitation to touching). The majority of the

youth (63%) were charged with offenses in the violent but not sexual category, 23% in the non-

violent category, and 14% in the sexual category.

Procedure

Participants’ data were coded from their assessment files, probation case notes, and

criminal records according to a previously-developed coding scheme (Peterson-Badali, Skilling,

& Haqanee, 2015). Assessment files were reviewed to extract information on demographics,

mental health, offense history, charges leading to referral for assessment, scores on the YLS/CMI

(the Youth Level of Service/Case Management Inventory; Hoge & Andrews, 2002), and

clinician recommendations targeting identified criminogenic needs. At the time of assessment, a

psychologist, a psychiatrist, or a social worker with expertise in forensic assessment completed

the YLS/CMI and the court-ordered assessments using information from youth’s legal history,

standardized questionnaires and psychological tests, collateral reports from other sources (e.g.,

parents, probation officers, youth workers), and youth self-report (Peterson-Badali et al., 2015).

Probation case notes were reviewed to determine the details of youth’ sentence, case

management received during probation, and whether services youth received matched

recommendations made in clinical files. Recidivism information was obtained from criminal

records for youth with at least a two year follow-up period following their assessments.

8

Measures

(1) Mental disorder diagnoses

Mental disorder diagnoses, extracted from the clinical files, were made by either a

psychiatrist or a licensed psychologist who was part of the multidisciplinary team that conducted

the forensic assessments. There were six mental disorder variables in this study: disruptive

behavior disorders (DBD), Attention Deficit Hyperactivity Disorder (ADHD), Substance Use

Disorders (SUD), internalizing disorders, Learning Disorders (LD), and cognitive disabilities.

Disruptive behavior disorders include Oppositional Defiant Disorder (ODD) and Conduct

Disorder (CD). SUD include substance abuse disorder and substance dependence disorder.

Internalizing disorders include mood and/or anxiety disorders such as major depressive disorder,

dysthymia, generalized anxiety disorder, and post-traumatic stress disorder. LD includes specific

learning disorders and communication disorders. Cognitive disabilities include intellectual

disabilities and global developmental delay. The diagnostic criteria used by the clinicians for

these mental disorders was drawn from the Diagnostic and Statistical Manual of Mental

Disorders-IV-TR edition (American Psychiatric Association, 2000) and Learning Disabilities

Association of Ontario (LDAO, 2001). Learning disabilities and cognitive disabilities have not

always been included in studies examining mental health issues in justice-involved youth;

however, these two groups of disorders have established links with justice-system involvement

(Mauser 1974; Simpson & Hogg, 2001) and further, given the high rates of learning and

cognitive disabilities within this population of youth, any discussion of mental health in justice-

involved youth would be incomplete without them.

(2) Risk to reoffend and criminogenic needs

The seven dynamic criminogenic needs were coded into seven binary variables based on

clinician recommendations from the assessment reports, which were informed by the YLS/CMI

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(Hoge & Andrews, 2002, 2011), a standardized forensic tool used to assess 12- to 18-year-old

youth’ risk to reoffend, criminogenic needs, and responsivity factors. The first section is a 42

item checklist divided into eight domains to assess youth’ risk to reoffend and criminogenic

needs. The eight domains map onto eight major risk factors relevant to criminal behavior as

identified in the RNR literature (Andrews & Bonta, 1990, 2010). These domains include: prior

and current criminal offenses (a major but static risk factor) and seven dynamic criminogenic

needs domains (family circumstances/parenting, education/employment, peer relations,

substance abuse, leisure/recreation, personality/behavior, and attitudes/orientation). Items within

each domain are summed into domain scores, which are also assigned categorical descriptors

(low, medium, high). An overall score is determined by summing all items; total scores also

categorize youth as low, moderate, high, or very high risk to reoffend.

The YLS/CMI has been reported to possess moderate to strong internal consistency for

most subscales (Catchpole & Gretton, 2003; Schmidt, Hoge, & Gomes, 2005; Vitopoulos,

Peterson-Badali, & Skilling, 2012). Moderate to strong concurrent validity has also been

reported between YLS/CMI total score and broad and narrow-band scores on the Child Behavior

Checklist (Schmidt et al., 2005) and Structured Assessment of Violence Risk in Youth

(Catchpole & Gretton, 2003). Predictive validity is reported to be moderate to high, with

YLS/CMI total scores significantly correlated with general and violent recidivism (Catchpole &

Gretton, 2003; Olver, Stockdale, & Wormith, 2009; Onifade et al., 2008; Rennie & Dolan, 2010).

In the current sample, inter-rater reliabilities for the YLS/CMI total score among the primary

clinicians ranged from .80 to .98 (average r = .93).

(3) Matching of identified criminogenic needs

Following procedures from previous studies (Peterson-Badali et al., 2015; Vitopoulos et

al., 2012), the extent to which youths’ identified criminogenic needs were targeted through

10

intervention and/or case management was examined using the concept of “treatment match”. For

each criminogenic need domain in which a youth had an identified need (i.e., a clinician

recommendation), match was coded as a binary variable (i.e., matched or not matched) by

reviewing the youth’s probation records to determine whether the need had been addressed via

service provision and/or case management activities. For example, a match in the Substance Use

domain meant that a youth successfully completed most or all of a substance abuse counseling

program recommended by a clinician. Other examples of match include completion of evidence-

based programs targeting clinician-identified Attitude and/or Personality needs, enrolment and

completion of school following recommendations in the Education domain, and association with

pro-social peers following recommendations in the Peer Relations domain.

For each youth an ‘overall match’ score was also calculated by dividing the number of

matched needs by the number of identified needs. For example, if a youth had two out of four

identified criminogenic needs addressed during probation, the youth’s overall match score was

50%.

(4) Recidivism

Recidivism was defined as whether a youth was convicted of one or more offenses within

a two year follow-up period following referral for assessment. Information was obtained from a

national police criminal record database. Reoffenses were only included for new offenses

occurring at least 3 months after forensic assessment completion in order to allow sufficient time

for probation service implementation.

Statistical analyses

(1)Mental disorder profiles

A two-step cluster analysis (SPSS Statistics/IBM Version 22, 2013) – which handles

categorical data and automatically tests multiple cluster solutions, outputting the optimal solution

11

– was used to determine patterns of mental disorder. Two-step cluster analysis employs a two-

stage process: In the first pre-clustering stage, each record in the dataset is scanned to determine

whether it is to merge with previously constructed dense regions or to form a new cluster by

itself based on a Log-likelihood distance measure. In the second stage, a hierarchical clustering

algorithm is applied to combine data records sequentially to form clusters (Chiu, Fang, Chen,

Wang, & Jeris, 2001; Mooi & Sarstedt, 2011). The six mental disorder variables described above

were used in the analysis: disruptive behavior disorders, ADHD, SUD, internalizing disorders,

LD, and cognitive disabilities. After the cluster analysis was completed, individuals within each

cluster were assigned a membership category, and subsequent analyses were conducted based on

the cluster group memberships.

(2) Treatment match across clusters

Overall treatment match scores were compared across the cluster groups using one-way

ANOVA tests. Post-hoc test Tukey was used to evaluate significant ANOVA results. Chi-square

tests were used to compare clusters within each criminogenic need domain, and Fisher’s exact

test was used in cases with small sample sizes.

(3) Predicting recidivism

A hierarchical binary logistic regression was conducted with recidivism as the outcome

variable. The predictors included individuals’ YLS/CMI domain scores for Criminal History,

overall match scores, and mental disorder cluster membership.

RESULTS

Preliminary analyses

Table 1 presents the descriptive statistics for participants’ demographic information,

offense information, and rates of psychiatric diagnoses. In the current sample, 67.2% of the

participants met diagnostic criteria for two or more mental disorder diagnoses. The most

12

common diagnoses included disruptive behavior disorders (74.9%), which included individuals

with CD and/or ODD diagnoses. This is followed by ADHD (43.1%), substance use/abuse

disorders (37.4%), LD (25.6%) , cognitive disabilities (10.3%), and internalizing disorders

(23.1%).The overall recidivism rate was 63%, and mean time to first reoffense conviction was

approximately 17 months for youth who reoffended.

Table 1: Participants’ demographic, offense, and psychiatric information

Variable Total (N = 195)

% Ethnicity (n)

Black 32.2 (63)

White 27.2 (53)

Asian 7.2 (14)

Other 23.6 (46)

Missing 9.7 (19)

% Index offense (n)

Non-violent 22.6 (43)

Violent (non-sexual) 63.1 (123)

Sexual 13.8 (27)

Psychiatric diagnoses (n)

Any two or more diagnoses 67.2 (131)

Any disruptive behavior disorders 74.9 (146)

Conduct disorder 72.8 (142)

Oppositional defiant disorder 10.8 (21)

Attention deficit hyperactivity disorder 43.1 (84)

Substance use/abuse diagnosis 37.4 (73)

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Learning disabilities 25.6 (50)

Cognitive disabilities 10.3 (20)

Internalizing disorder 23.1 (45)

Any mood disorder 19.0 (37)

Any anxiety disorder 10.8 (21)

Recidivism-yes (n) 63.1 (123)

Time to recidivism conviction in days for youth who

reoffended(s.d.)

508 (300.4)

Mental illness profiles

The cluster analysis resulted in a six cluster solution. The first cluster included individuals with

primarily LD diagnoses (n = 33, 16.9% of the total sample). The second cluster included

individuals with disruptive behavior disorders and internalizing disorders (DBD & Internalizing,

n = 38, 19.5% of the total sample). The third cluster included individuals with ADHD and

disruptive behavior disorders diagnoses (DBD & ADHD, n = 24, 12.3% of the total sample). The

fourth cluster included individuals with a disruptive behavior disorders, LD, and ADHD

diagnoses (DBD, LD, ADHD, n = 28, 14.4% of the total sample). The fifth cluster included

individuals with a disruptive behavior disorders, SUD, and ADHD diagnoses (DBD, SUD,

ADHD, n = 36, 18.5% of the total sample). The sixth cluster included individuals with primarily

disruptive behavior disorders diagnoses (n = 36, 18.5% of the total sample). Table 2 presents the

recidivism rates for the mental disorder clusters; a chi-square analysis resulted in a significant

14

model, 2 (5) = 11.03, p = .05. It appears that clusters 4 (DBD, LD, ADHD) and 6 (DBD) have

significantly higher rates of offending than clusters 1 (LD) and 2 (DBD & Internalizing

Disorders).

Are identified criminogenic needs being met at a similar rate across the mental disorder

clusters?

Table 2 displays the percentage of youth for whom a criminogenic need was identified and

matched during probation within each criminogenic needs domain and across mental disorder

clusters. The overall proportion of treatment match varied from 17% (in the peer relations

domain) to 35% (in the family domain). When examined across the mental disorder clusters, peer

relations, leisure, and antisocial attitudes consistently displayed the lowest proportions of

treatment match. There were significant differences across the mental disorder clusters with

respect to overall treatment match scores (F(5,189) = 3.66, p = .004, η²= 0.09). Individuals in

cluster 2 (DBD, Internalizing) (M = .43, SD = .33) were more likely to have their identified

needs successfully addressed compared to individuals in cluster 5 (DBD, SUD, ADHD) (M = .17,

SD = .26) and 6 (DBD) (M = .22, SD = .25). There were no significant differences across the

other mental disorder clusters. When criminogenic needs were examined individually, Family

was the only domain with significant differences across the clusters, with clusters 5 (DBD, SUD,

ADHD) and 6 (DBD) less likely to have their need matched compared to the other clusters.

Table 2: Percentage treatment match scores within each criminogenic need and recidivism rates across mental disorder clusters

Variables

Cluster 1

(LD)

Cluster 2

(DBD &

Internalizing)

Cluster 3

(DBD &

ADHD)

Cluster 4

(DBD, LD,

& ADHD)

Cluster 5

(DBD,

SU/A/D, &

ADHD)

Cluster 6

(DBD)

Total

Family 37.0 (10) 44.1 (15) 52.4 (11) 50.0 (13) 18.8 (6) 12.9 (4) 34.5 (59)

Education/Employment 29.0 (9) 45.9 (17) 31.8 (7) 32.1 (9) 20.6 (7) 41.7 (15) 34.0 (64)

Peer Relations 23.8 (5) 4.8 (1) 26.9 (7) 25.0 (6) 11.4 (4) 11.8 (4) 16.8 (27)

Substance Use 41.7 (5) 7.7 (1) 36.4 (8) 25.0 (5) 17.1 (6) 5.3 (1) 21.5 (26)

Leisure 39.1 (9) 15.0 (3) 30.0 (9) 19.0 (4) 10.7 (3) 16.1 (5) 21.6 (33)

Personality 30.8 (8) 34.8 (8) 58.6 (17) 28.6 (8) 21.9 (7) 30.3 (10) 33.9 (58)

Attitude 25.0 (2) 11.8 (2) 31.6 (6) 20.0 (4) 16.7 (4) 12.5 (4) 18.3 (22)

Reoffended 48.5 (16) 50.0 (19) 66.7 (16) 78.6 (22) 63.9 (23) 75.0 (27) 63.1 (123)

Does matching services to identified criminogenic needs predict recidivism equally well

across the mental disorder clusters?

To address this question I first examined the predictive validity of the YLS/CMI as a risk

measure in this sample. A logistic regression analysis resulted in a significant model, 2 (1) =

12.97, p < .001; and its predictor, YLS/CMI total score, was also significant, B = .07, 2 (1) =

12.04, p = .001, odds ratio (OR) = 1.07, 95% confidence interval (CI) = [1.03, 1.11]. Therefore,

for each unit increase in YLS/CMI score, the odds of reoffense were 7% greater. Additionally, a

Receiver Operating Characteristic (ROC) analysis was conducted to examine the ability of the

model to correctly classify individuals. The area under the curve (AUC) statistic was significant

(0.66, p < .001), indicating that YLS/CMI scores classified the sample significantly better than

chance; at a 95% CI, there was a 66% probability that a randomly selected recidivist would

obtain a higher YLS/CMI score than a randomly selected non-recidivist (CI range = 0.57-0.74).

Next, a hierarchical logistic regression with recidivism as the outcome was conducted to

examine whether matching services to identified criminogenic needs predicted recidivism

equally well across the mental disorder clusters. YLS/CMI Criminal History domain scores were

first entered into the regression model in lieu of overall YLS/CMI scores to control for risk

because: 1) static factors such as offense history are strong predictors of reoffending (Bonta, Law,

& Hanson, 1998; Cottle, Lee & Heilburn, 2001), and 2) the overall treatment match scores

(which are entered in the second step of the hierarchical logistic regression) were calculated from

the total YLS/CMI risk scores, resulting in an overlap if YLS/CMI total scores were used

(Vitopoulos et al., 2012). Furthermore, Criminal History domain scores and YLS/CMI total risk

scores were highly correlated, r (236) = .65, p < .001, making Criminal History an appropriate

proxy for YLS/CMI total scores. In the second step, overall treatment match scores for each

17

participant were entered into the model. In the third and final step, mental disorder clusters were

entered into the model, with cluster 1 (LD) as a reference group because of its ‘low risk’ status.

The model was significant at every step (see Table 4). Criminal history was significant at

every step, with higher scores associated with an increase in recidivism. Matching services to

clinician identified needs was also significant at every step, with higher overall treatment match

scores associated with decreased odds of recidivism. With respect to the mental disorder clusters,

clusters 4 (DBD, LD, ADHD) and 6 (DBD) significantly predicted increased odds of recidivism

when compared to cluster 1 (LD).

In order to understand why clusters 4 (DBD, LD, ADHD) and 6 (DBD) predicted

increased odds of recidivism even after controlling for Criminal History scores and treatment

match, the compositions of the clusters were explored in more detail. In particular, given that the

‘disruptive behavior disorders only’ cluster significantly predicted recidivism (and was

associated with higher reoffence rates than most of the other clusters), it seemed prudent to

examine specific YLS/CMI items related to disruptive behavior disorders across the mental

disorder clusters. The items included Inadequate guilt feelings under the Antisocial Personality

criminogenic need domain, as well as Antisocial/procriminal attitudes, Not seeking help, Actively

rejecting help, and Callous/little concern for others under the Antisocial Attitude criminogenic

need domain. Chi-square analyses revealed significant differences across the mental disorder

clusters for each of these YLS/CMI items (see Table 4). Compared to clusters 1 (LD) and 2

(DBD & Internalizing Disorders), a higher percentage of individuals in cluster 6 (DBD) were

identified by clinicians as presenting Inadequate guilt feelings and Not seeking help. Compared

to cluster 1 (LD), a greater percentage of youth in clusters 4 (DBD, LD, ADHD) and 5 (DBD,

SUD, ADHD) had the Antisocial/procriminal attitudes item checked off by clinicians. Compared

to cluster 1 (LD), a greater percentage of youth in cluster 4 (DBD, LD, ADHD) had the Actively

18

rejecting help item endorsed by clinicians. Compared to cluster 1 (LD), a greater percentage of

youth in clusters 3 (DBD & ADHD), 4 (DBD, LD, ADHD), 5 (DBD, SUD, ADHD), and 6

(DBD) were identified as Callous/little concern for others item; cluster 6 (DBD) was also more

likely to have this item checked off than cluster 2 (DBD & Internalizing Disorders).

Table 3: Hierarchical regression analyses predicting recidivism from criminal history, match, and mental disorder profiles

Model Variables Β SEβ Wald’s χ2 Df p exp(B)

Lower

CI

(95%)

Upper

CI

(95%)

Model (Step) 1

Criminal history 0.47 0.09 28.26 1 <.001 1.60 1.35 1.90

Constant -0.43 0.19 4.94 1 .03 0.65

Overall model at Step 1

33.42 1 <.001

Model (Step) 2

Criminal history 0.40 0.09 18.64 1 <.001 1.48 1.24 1.78

Overall treatment match -1.77 0.49 13.15 1 <.001 0.17 0.07 0.44

Constant 0.27 0.27 0.97 1 .33 1.31

Overall model at Step 2

47.35 2 <.001

Model (Step) 3

Criminal history 0.44 0.10 17.89 1 <.001 1.55 1.27 1.91

Overall treatment match -1.75 0.53 11.04 1 .001 0.17 0.06 0.49

20

Cluster 2 0.31 0.46 0.47 1 .49 1.37 0.56 3.34

Cluster 3 0.49 0.57 0.73 1 .39 1.62 0.54 4.93

Cluster 4 1.20 0.55 4.79 1 .03 3.33 1.13 9.79

Cluster 5 -0.45 0.52 0.77 1 .38 0.64 0.23 1.75

Cluster 6 1.06 0.49 4.72 1 .03 2.88 1.11 7.47

Constant -0.13 0.34 0.16 1 .69 0.88

Overall model at Step 3 59.91 7 <.001

21

Table 4: Disruptive behavior disorders related YLS/CMI items by mental disorder clusters

Variables

Cluster 1

(LD)

%(n)

Cluster 2

(DBD &

Internalizing)

%(n)

Cluster 3

(DBD &

ADHD)

%(n)

Cluster 4

(DBD,

LD, &

ADHD)

%(n)

Cluster 5

(DBD,

SU/A/D, &

ADHD)

%(n)

Cluster 6

(DBD)

%(n)

X2 df p V

7f. Inadequate guilt feelings 37.5 (12) 40.5 (15) 68.2 (15) 71.4 (20) 65.7 (23) 72.2 (26) 17.04 5 .004 .30

8a. Antisocial/procriminal

attitudes

22.6 (7) 29.7 (11) 40.9 (9) 60.7 (17) 62.9 (22) 44.4 (16) 17.17 5 .004 .30

8b. Not seeking help 34.4 (11) 43.2 (16) 63.6 (14) 64.3 (18) 54.3 (19) 75.0 (27) 15.04 5 .010 .28

8c. Actively rejecting help 9.4 (3) 27.0 (10) 22.7 (5) 50.0 (14) 31.4 (11) 30.6 (11) 12.74 5 .026 .26

8e. Callous, little concern for

others

6.3 (2) 16.2 (6) 45.5 (10) 42.9 (12) 48.6 (17) 41.7 (15) 23.03 5 <.001 .35

DISCUSSION

The present study aimed to further understand the role of mental health in offending by

examining mental disorder profiles in justice-involved youth in the context of the risk-need-

responsivity framework. To this end, interrelationships among mental disorder clusters, risk

levels, criminogenic needs, case management, and recidivism were explored.

Consistent with previous findings of high comorbidity rates in justice-system involved

(Abram et al., 2003; Abrantes et al., 2004; Shufelt & Cocozza, 2006; Vermeiren, 2003), over 67%

of the youth in the current sample exhibited two or more concurrent diagnoses. Not surprisingly,

disruptive behavior disorders was the most frequently diagnosed mental disorder, followed by

ADHD, and substance use disorders. The base rates of mental disorder diagnoses found in the

current study were consistent with those found in the wider literature. For example, 43% of the

sample met diagnostic criteria for ADHD, which falls within the typical range observed in

justice-involved youth (Lederman, Dakof, Larrea, & Li, 2004; Shufelt & Cocozza, 2006; Teplin

et al., 2007; Vreugdenhil et al., 2004). Results from the current study confirm previous findings

that comorbidity of mental disorders is the norm, rather than the exception, in this sample of

justice-involved youth.

Mental disorder profiles

Five of the six mental disorder clusters included disruptive behavior disorders, an

unsurprising finding given that antisocial and/or criminal behaviors are features of diagnoses

such as conduct disorder. Disruptive behavior disorders and ADHD was the most prevalent of

the comorbid diagnoses, which was manifested in three of the six mental disorder clusters. This

finding is also congruent with the extant literature on comorbidities in the community and justice

populations, such that ADHD has been found to frequently overlap with CD (Angold et al., 1999;

Biederman, Newcorn, & Sprich, 1991; Vermeiren, 2003). This high comorbidity rate may reflect

23

similarities in the presentation of symptoms for these disorders, and/or synergies arising from the

interaction of these two disorders. For example, a youth with ADHD may, in a moment of

impulsivity, engage in reckless thrill seeking behaviors that introduce him to the justice system.

It may also be that the combination of symptoms represented in these two disorders puts

individuals at risk for engagement in antisocial acts (e.g., aggression, lying, impulsivity). For

example, ADHD has been more frequently found in children with early-onset, rather than late-

onset, CD (Loeber, Green, Keenan, & Lahey, 1995). Given that individuals with early-onset CD

often engage in persistent antisocial behaviors across the lifespan, concurrent diagnoses of

ADHD and disruptive behavior disorders may reflect intrinsic vulnerabilities in some youth that

explain their entries into the justice system.

One mental disorder profile included individuals with disruptive behavior disorders,

ADHD, and LD comorbidities. The overlap between LD and ADHD is a robust finding in the

general population (Biederman et al., 1991). Children and youth with both diagnoses have

similar difficulties related to learning and often exhibit poor academic achievement, leading to

disengagement from schools (Barry, Lyman, & Klinger, 2002; Marder & D’Amico, 1992;

Rapport, Scanlan, & Denney, 1999). In fact, the co-occurrence of both LD and ADHD is

associated with poorer outcomes such as more disruptions in cognitive abilities and poorer

performance in academic tasks compared to children with either ADHD or LD (Jakobson &

Kikas, 2007; Kamphaus & Frick, 2005; Mayes, Calhoun, & Crowell, 2000). Academic

underachievement and school disengagement are potent risk factors for delinquency and later

problematic behaviors (Henry, Knight, & Thomberry, 2012; Murray & Farrington, 2010).

Therefore, individuals with comorbid LD and ADHD may be at higher risk for academic

underachievement than those with either LD or ADHD diagnoses alone, and combined with

DBD, predispose them to engage in antisocial behaviors.

24

Another mental disorder cluster with disruptive behavior disorders and ADHD also

included substance use/abuse disorders. Substance use/abuse disorders are highly prevalent in

justice-involved youth (Abram et al., 2003; Abrantes et al., 2004) and their presence is related to

more severe antisocial behaviors and higher incidence of psychiatric comorbidity (Vermeiren,

2003). The aggregation of substance abuse, CD and ADHD may be an especially hazardous

mixture, as substance abusers tend to demonstrate more aggressive CD and more severe ADHD

than non-abusers (Milin et al., 1991), and they tend to start using substances at earlier ages

(Thompson, Riggs, Mikulich, & Crowley, 1996)). Therefore, the co-occurrence of these three

diagnoses may represent a complex developmental relationship that is especially relevant for

justice-involved youth.

The disruptive behavior disorders and internalizing disorders cluster primarily included

individuals with a disruptive behavior disorders and mood and/or anxiety disorders. The

internalizing disorders included a varied array of diagnoses, such as major depressive disorders,

dysthymia, generalized anxiety disorders, and/or post traumatic stress disorders. There is some

evidence from the literature indicating a tendency for CD to co-occur with both depressive and

anxiety disorders in children and adolescents in the general (Zoccolillo, 1992) and clinical

populations (Russo & Beidel, 1994). In fact, increasing severity of antisocial behaviors is

associated with increasing risk for affective disorders (Zoccolillo, 1992), and recently one study

has reported that justice-involved youth with both disruptive behavior disorders and internalizing

disorders are at an increased risk for recidivism compared to non-disordered justice-involved

youth (Hoeve et al., 2013). Therefore, this mental disorder profile may represent a group of

youth with more severe antisocial behaviors and poorer outcomes.

Two mental disorder clusters emerged consisting primarily of single diagnoses. The LD

cluster included youth with varied impairments in aspects of learning ranging from reading,

25

mathematics, to language. Individuals with LD are at a higher risk for involvement in the justice

system, and three hypotheses have been put forward to explain the link (Brier, 1989; Keilitz &

Dunivant, 1986). The susceptibility hypothesis proposes that individuals with LD possess certain

cognitive and neurological characteristics (e.g., inability to anticipate the consequences actions,

poor perception of social cues) that leave them vulnerable to engagement in antisocial activities

(Murray, 1976). The school failure hypothesis proposes that learning disability leads to academic

failure, which contributes to negative self-image, frustration, and school dropout, in turn leading

to antisocial behaviors (Murray, 1976). The differential treatment hypothesis proposes that the

justice system treats individuals with LD more harshly than individuals without LD (Dunivant,

1982). Current literature suggests that a multifactorial explanation – combining factors from the

three hypotheses – affect the relationship between learning disability and youth delinquency

(Brier, 1989).

The disruptive behavior disorders -only cluster consisted of individuals with CD and/or

ODD diagnoses, with a defining feature in antisocial behaviors. By virtue of their inclusion in

the justice system, all youth in the current sample have displayed antisocial behaviors. However,

given that most of the youth in the sample exhibited psychiatric comorbidities, the fact that youth

in this cluster have predominantly disruptive behavior disorders diagnoses without other

psychopathological complications makes them a unique group for comparison and study.

Despite limited research in psychiatric comorbidities in justice-involved youth, the mental

disorder profiles that emerged from the current study are consistent with the extant literature

from the justice-involved, as well as general child and adolescent, populations. Thus, these six

mental disorder profiles reflect robust findings that are relevant for the treatment and

rehabilitation of justice-involved youth (discussed below).

Matching services to criminogenic needs

26

In addition to identifying mental disorder profiles, it is also important to examine their

relationships to justice-involved youth. To our knowledge, no research has investigated mental

disorder clusters in the context of the RNR framework. The RNR need principle stipulates that

services must target identified criminogenic needs in order to reduce risk (Andrews et al., 1990).

One of my goals was to understand whether mental disorder profiles were associated with

delivery of such services, or “treatment matching” (i.e., addressing identified criminogenic needs

through probation-directed case management). I found that youth in cluster 2 (disruptive

behavior disorders & internalizing disorders) had higher proportion of needs matched compared

to youth in cluster 5 (disruptive behavior disorders, SUD, ADHD) and 6 (disruptive behavior

disorders). One explanation for this finding relates to variability in the availability of resources

for targeting different criminogenic needs. Few programs and services exist that target the

criminogenic need of Antisocial Attitude, which includes features coinciding with the diagnostic

criteria for disruptive behavior disorders. In the current study, Antisocial Attitudes displayed one

of the lowest proportion treatment match across the mental disorder clusters. These findings fall

in line with previous research, in which lack of programming was identified by probation

officers as a significant barrier to treatment (Haqanee, Peterson-Badali, & Skilling, 2015). In

contrast, resources that target the criminogenic need of Education are more abundant, including

special education programs, alternative schools, cooperative education programs, and vocational

programs. In this study, Education was one of the criminogenic needs with the highest proportion

of treatment match across the mental disorder clusters, which coincides with previous research

findings that probation officers may “over-program” the criminogenic need of Education (i.e.,

targeting Education when it was not identified as a relevant need) (Haqanee et al., 2015; Luong

& Wormith, 2011). Thus, successful matching of services to identified criminogenic needs may

be dependent on the nature of the needs. Individuals in clusters 5(disruptive behavior disorders,

27

SUD, ADHD) and 6 (disruptive behavior disorders) exhibited higher needs in Antisocial Attitude

domains, one of the criminogenic needs with limited programming, which may explain their

diminished proportion match.

Mental disorder profiles and recidivism

Consistent with previous research on similar samples, youths’ reoffending was

significantly predicted by their criminogenic risk (as measured by Criminal History) as well as

by the extent to which their identified needs were addressed during their probation terms overall

treatment match (Peterson-Badali et al., 2015; Vieira et al., 2009). In the current study – even

after accounting for risk and treatment matching – the specific nature of the mental disorder

clusters also differentiated individuals with respect to recidivism outcomes. Individuals in

clusters 4 (disruptive behavior disorders, LD, ADHD) and 6 (disruptive behavior disorders) had

higher odds of reoffense than those in cluster 1 (LD), which was chosen as the reference group

due to its consistently low risk scores. In addition, the observed rates of recidivism were also

elevated for clusters 4 (disruptive behavior disorders, LD, ADHD) and 6 (disruptive behavior

disorders) compared to other clusters, such as 1 (LD), and 2 (disruptive behavior disorders &

internalizing disorders).

The comorbidity profile of disruptive behavior disorders, LD, and ADHD (cluster 4) in

justice-involved youth is not surprising given that high comorbidity rates of disruptive behavior

disorders with ADHD (Vermeiren, 2003), and LD with ADHD (Biederman et al., 1991) have

been long established in the forensic and general literature. However, the particular profile of

disruptive behavior disorders, LD, and ADHD has hitherto not been emphasized as an important

mental disorder profile for the justice population. This profile’s inclusion of both LD and ADHD

suggests that individuals in this group likely demonstrate poor academic achievement (Barry et

al., 2002), and poor academic achievement has been linked to behavioral problems and higher

28

rates of recidivism in justice-involved adolescents (Katsiyannis, Ryan, Zhang, & Spann, 2008).

However, if the link between academic achievement and delinquency is robust, individuals in

cluster 1 (LD) should also reoffend at similar or higher rates compared to other clusters, but this

was not the case since cluster 1 demonstrated the lowest recidivism rate out of the mental

disorder clusters. Given the disruptive behavior disorders, LD, and ADHD cluster, in particular,

predicted recidivism, there must be characteristics at play other than academic underachievement.

In a study where justice-involved youth with LD were followed longitudinally, it was found that

specific personality characteristics of impulsivity and poor judgment discriminated between

persisting and non-persisting antisocial behaviors (Waldie & Spreen, 1993). Therefore, it appears

that the unique combination of ADHD and LD, in the presence of disruptive behavior disorders

increases an individual’s likelihood for reoffending, and this comorbidity pattern warrants further

investigation.

Cluster 6 (disruptive behavior disorders) also predicted an increased odds of reoffending in

the current study. In order to explore this finding further, YLS/CMI items related to disruptive

behavior disorders were examined. Clinicians identified the YLS/CMI antisocial attitude items

inadequate guilt feelings, not seeking help, and callous/little concern for others for a greater

percentage of individuals in cluster 6 than for individuals in clusters 1 (LD) and 2 (disruptive

behavior disorders & internalizing disorders). This differential rate of identification of YLS/CMI

items is particularly interesting given that individuals in cluster 2 and 6 both have disruptive

behavior disorders diagnoses. Clinicians also identified the items inadequate guilt feelings (e.g.,

toward victims), antisocial attitudes, not seeking help, actively rejecting help, and callous/little

concern for others for a greater percentage of youth in cluster 4 (disruptive behavior disorders,

LD, ADHD) than for youth in cluster 1 (LD). Clinician identification of these items suggests that

youth in clusters 4 and 6 would qualify for the callous-unemotional specifier (e.g., lack of guilt

29

or remorse, callous-lack of empathy) under the current Conduct Disorder diagnostic criteria of

the Diagnostic and Statistical Manual of Mental Disorders-5th

edition (American Psychiatric

Association, 2013). Studies have found that youth with callous-unemotional traits engage in

particularly severe, stable, and aggressive patterns of antisocial behaviors (Frick & White, 2008;

Moran, Ford, Butler, & Goodman, 2008), which may explain the increased rates of recidivism in

clusters 4 and 6. These findings suggest that clinicians should go beyond diagnosing mental

disorders to understanding the specific subtypes and specifiers related to individuals’ diagnoses.

In this case, understanding whether justice-involved youth endorse callous-unemotional traits

may improve treatment planning and case management outcomes.

Implications for risk, need, and responsivity

Consistent with the RNR principles and previous research (Catchpole & Gretton, 2003;

Olver et al., 2009; Peterson-Badali et al., 2015), risk (as assessed by the YLS/CMI) was

predictive of youths’ reoffending, and addressing identified criminogenic needs through

interventions was associated with reduced rates of recidivism. In addition to adding to the body

of literature supporting the risk and need principles, this study also identified commonly

occurring mental disorder profiles in a sample of justice-involved youth, in which two profiles

(clusters 4 and 6) emerged as significant predictors of youths’ recidivism even after controlling

for risk and treatment matching. The latter finding is noteworthy given that the central tenets of

the RNR framework rest on criminogenic factors as necessary and sufficient to predict

reoffending and critical to treatment planning. Differences between clusters in the frequency

with which clinicians endorsed YLS/CMI antisocial attitude items suggest that it may be

important for case managers and treatment providers to gain finer grained mental health

information (i.e., specifiers of mental disorder diagnoses) as well as attend to specific YLS/CMI

30

items in order to inform and fine-tune intervention planning and case management for justice-

involved youth.

Strengths and limitations

Data used in the current study were collected from comprehensive assessments, often

involving multiple parties such as justice-involved youth, their parents, and other collateral

sources of information (e.g., frontline workers). This is a methodological strength as it provided

multiple sources of information regarding youths’ mental health functioning, which was used to

arrive at psychiatric diagnoses. Past research on justice-involved youth’ psychopathology heavily

relied on self-report measures (Vermeiren, 2003), which alone is not a reliable way to arrive at

mental disorder diagnoses, especially with respect to disruptive behavior disorders. Furthermore,

past research mainly focused on understanding single psychiatric diagnoses and their impacts on

youth’ outcomes. When comorbidities were examined in the justice-involved population, the

focus was often placed on understanding their prevalence rates. This study is one of the few that

examined psychiatric profiles in justice-involved youth and attempted to understand the

significance of such profiles in relation to the RNR model and youth’ reoffending.

The current study also had several limitations. The justice-involved youth sample was

drawn from a single mental health care facility in an urban area in Canada. Gender ratio in this

sample was heavily skewed, with nearly three times as many male as female participants.

Disproportionate gender ratio precluded the examination of mental disorder profiles by gender,

which is unfortunate given the documented differences in the manifestation of psychopathologies

in male and female justice-involved individuals (Abram et al., 2003). Furthermore, the current

study’s modest sample size also prevented more detailed classification of mental disorders. For

example, the low numbers of anxiety and mood disorders in the current sample necessitated the

creation of an overall internalizing disorder category; such amalgamation may have concealed

31

important differences between specific internalizing disorders with respect to youth’ risk, needs,

and/or outcomes. Therefore, future research should aim to draw larger samples from

geographically diverse areas with more balanced gender ratios in order to replicate and extend

the findings from this study.

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