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Running Head: BARRIERS TO RNR IMPLEMENTATION

BARRIERS TO EFFECTIVE RNR IMPLEMENTATION

FOR YOUTH IN THE JUSTICE SYSTEM

by

Anjani Kapoor

A thesis submitted in conformity with the requirements

for the degree of Masters of Arts

Graduate Department of Applied Psychology and Human Development

Ontario Institute for Studies in Education

University of Toronto

© Copyright by Anjani Kapoor (2015)

BARRIERS TO RNR IMPLEMENTATION ii

Barriers to Effective RNR Implementation for Youth in the Justice System

Anjani Kapoor

Master of Arts

Applied Psychology and Human Development

University of Toronto

2015

Abstract

Despite robust evidence of the efficacy of the RNR framework, studies indicate that

the needs of youth on community supervision as identified in risk-need assessments are

frequently not reflected in the services they receive. Potential barriers to treatment were

examined in 219 Canadian youth serving probation terms. Ninety percent of youth had a

barrier to treatment (M = 4.83 SD = 2.81), with lifestyle destabilizers, engagement issues,

parental factors, and probation officers’ case management decisions as the most common

categories of barriers. Engagement issues and systems factors were the barrier categories that

decreased the likelihood of receiving treatment in the greatest number of criminogenic need

domains. Furthermore, barrier categories were related to the likelihood of recidivism.

Results call for greater adherence to the Specific Responsivity Principle, expansion to

include the assessment and management of destabilizing factors, and attention to larger

issues such as the need for a responsive system.

BARRIERS TO RNR IMPLEMENTATION iii

Table of Contents

Table of Contents…………………………………………………………………………………iii

List of Tables .................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

List of Appendices ....................................................................................................................... viii

Introduction and Overview .............................................................................................................. 1

Overview of the RNR Framework ............................................................................................ 2

Implementation of the RNR Framework ................................................................................... 4

Barriers to implementation. .................................................................................... 5

The Current Study ..................................................................................................................... 8

Method ............................................................................................................................................. 9

Sample and Procedure ............................................................................................................... 9

Measures and Coding .............................................................................................................. 10

Risk to reoffend and criminogenic needs. ............................................................ 10

Service-to-needs match ........................................................................................ 11

Potential Barriers to Service. ................................................................................ 12

Recidivism. ........................................................................................................... 12

Results ........................................................................................................................................... 13

Preliminary Analyses ............................................................................................................... 13

Question 1: What are potential barriers to treatment for community sentenced youth? ......... 14

Lifestyle destabilizers. .......................................................................................... 14

BARRIERS TO RNR IMPLEMENTATION iv

Clinical destabilizers ............................................................................................ 16

Capacity issues ..................................................................................................... 16

Engagement issues ............................................................................................... 17

Parental factors. .................................................................................................... 17

Systems factors ..................................................................................................... 17

Case management decisions. ................................................................................ 18

Barrier composites.. ............................................................................................. 18

Question 2: How does a youth’s total number of barriers relate to their crime, risk level, and

recidivism outcomes? ............................................................................................................... 19

Question 3: To what extent are barriers to treatment associated with criminogenic need

match? ....................................................................................................................................... 21

Family. ................................................................................................................. 21

Education. ............................................................................................................. 22

Employment. ........................................................................................................ 22

Personality. ........................................................................................................... 22

Leisure .................................................................................................................. 23

Peers. .................................................................................................................... 23

Attitude and substance use.. ................................................................................. 23

Overall match.. ..................................................................................................... 24

Question 4: Do barriers to treatment account for recidivism beyond static risk and overall

treatment match? ....................................................................................................................... 25

Discussion ...................................................................................................................................... 26

Understanding Barriers to Treatment ...................................................................................... 28

BARRIERS TO RNR IMPLEMENTATION v

Barriers to Treatment and Risk Level ..................................................................................... 30

Barriers to Treatment and Criminogenic Need Match ............................................................ 32

Barriers to Treatment and Recidivism ..................................................................................... 36

Limitations and Future Directions ................................................................................................. 37

References ..................................................................................................................................... 39

BARRIERS TO RNR IMPLEMENTATION vi

List of Tables

Table 1: Demographic and Criminal History Characteristics by Criminogenic Match

Category…………………………………………………………………………….……………21

Table 2: Composites and Barriers to Treatment………………………………………………....23

Table 3: Bivariate Correlations Between Barrier Composites, Overall Match And Criminal

History ……………………………………………...…………………………………………....27

Table 4: Mean Number of Barriers per Youth Based on Inclusive and Non-criminogenic

Categories for Categories of Offense, Risk Level, and Recidivism Outcomes…………….……28

Table 5: Summary of Hierarchal Regression Analysis – Overall Match Predicted by Criminal

History and Seven Barrier Composites (n = 219) …………………………………………...…..32

Table 6: Hierarchal Logistic Regression – Recidivism Predicted by Criminal History, Overall

Criminogenic Need Match, and Seven Barrier Composites (n = 219)…………………….…….34

BARRIERS TO RNR IMPLEMENTATION vii

List of Figures

Figure 1: Summary of Significant Regression Findings by Barrier Category…………………28

BARRIERS TO RNR IMPLEMENTATION viii

List of Appendices

Appendix A: Logistic Regression – Family Match Predicted by Criminal History and Seven

Barrier Composites (n = 181)……………………………………………………………………50

Appendix B: Logistic Regression – Education Match Predicted by Criminal History and Seven

Barrier Composites (n = 201) …………………………………………………………………...51

Appendix C: Logistic Regression – Employment Match Predicted by Criminal History and

Seven Barrier Composites (n = 104) ……………………………………………………………52

Appendix D: Logistic Regression – Substance Abuse Match Predicted by Criminal History and

Seven Barrier Composites (n = 123) ……………………………………………………………53

Appendix E: Logistic Regression – Personality Match Predicted by Criminal History and Seven


Appendix F: Logistic Regression – Attitude Match Predicted by Criminal History and Seven


Appendix G: Logistic Regression – Leisure Match Predicted by Criminal History and Seven


Appendix H: Logistic Regression – Peer Match Predicted by Criminal History and Seven Barrier

Composites (n = 173) ……………………………………………………………………………57

BARRIERS TO RNR IMPLEMENTATION 1

Introduction and Overview

Advances in the field of correctional psychology have documented the ineffectiveness of

punitive sanctions in reducing recidivism among youth as well as the success of appropriately

targeted rehabilitative approaches (Andrews & Bonta, 2010; Andrews, Bonta, & Hoge, 1990;

Loeber & Farrington, 1998; Lipsey & Wilson, 1998; Lipsey, 1999; McGuire & Priestly, 1995).

The Risk-Need-Responsivity (RNR) framework (Andrews et al., 1990) is a theoretically derived,

evidence-based model and guide to risk assessment and case management strategies to

successfully address youth crime. Research has shown that interventions adhering to the three

main principles in this framework (i.e., risk, need, and responsivity) produce significant

reductions in recidivism among youth (Andrews & Bonta, 2010; Vieira, Skilling, & Peterson-

Badali, 2009; Vitopoulos, Peterson-Badali, & Skilling, 2012; Wooditch, Tang, & Taxman,

2014). However, despite robust evidence of the efficacy of the RNR framework, there remains a

significant gap between theory and practice, with many community-sentenced youth not

receiving services targeted to their identified needs (Flores, Travis & Latessa, 2004; Haas &

DeTardo-Bora, 2009; Peterson-Badali, Skilling & Haqanee, 2015; Young, Moline, Farrell, &

Bierie, 2006).

To date, there is a dearth of research investigating barriers to providing youth with

treatment for their needs as identified through widely used RNR based risk/need assessments

during their community sentences. Factors that impede with treatment are likely to include, but

are not limited to, responsivity concerns – elements belonging to the principle that presents as the

most neglected of the framework (Bourgon & Bonta, 2014) – making this a fruitful area for

research. Examining the treatment gap in terms of barriers to service may help clarify issues that


impede the ability of frontline workers to implement proven recidivism reduction

interventions with their clients on a daily basis.

Overview of the RNR Framework

Paying heed to a youth’s likelihood to recidivate is the first step in achieving the goals of

desistance and rehabilitation. The Risk Principle states that levels of treatment services must be

matched to the risk level of the youth (Andrews & Bonta, 2010). Extensive and intensive

interventions should be reserved for higher risk youths, with minimal or no intervention being

sufficient for low-risk youths (Andrews & Bonta, 2010). Studies have shown that reductions in

recidivism are only found for high-risk individuals when they are provided with sufficiently

intensive services. Conversely, providing intensive treatment to or grouping low-risk and high-

risk individuals together sometimes produces deleterious effects by disrupting the low-risk

individuals’ positive social networks (Andrews & Friesen, 1987; Dowden & Andrews; 1999;

Lowencamp & Latessa, 2004).

The Need Principle of the RNR model states that treatment should focus on criminogenic

needs defined as dynamic (or changeable) risk factors that are strongly and directly associated

with recidivism.1 Results from meta-analytic studies draw distinctions between criminogenic

needs that are highly predictive of recidivism (i.e., antisocial personality pattern, procriminal

attitudes and procriminal associates) and those that are moderately predictive (i.e., family factors,

education/employment, leisure, and substance abuse). According to the RNR model,

criminogenic needs that are highly predictive of recidivism should be prioritized over those that

are moderately predictive and these should always be prioritized over noncriminogenic needs,

1 While static factors are indeed predictive of reoffending (Hoffman and Beck, 1980), these factors are inaccessible in treatment and are no more predictive than those factors of a dynamic nature (Gendreau, Goggin & Paparozzi, 1996).


factors that are weakly associated with recidivism, such as low self-esteem (Andrews &

Bonta, 2010). It is not stated in the RNR principles that these needs should be ignored

altogether, just that they should not be the primary focus of correctional rehabilitation, especially

without a primary focus on criminogenic needs.

According to the Responsivity Principle, measures should be taken to maximize youths’

ability to benefit from interventions designed to address criminogenic needs (Andrews & Bonta,

2010). Responsivity factors are dynamic factors that have a weak (if at all) direct association

with recidivism but may have an indirect impact on recidivism via their influence on the

effectiveness with which criminogenic needs are addressed. Although these factors are only

related to recidivism in a distal way, failure to consider them may prevent someone from

participating in an intervention, thereby leaving the underlying criminogenic needs or risk factors

unaddressed.

General responsivity refers to the delivering of treatment programs in a mode that is

consistent with the ability and learning style of the youth. Andrews and Bonta (2010) cite

cognitive-behavioral (CBT) and cognitive social learning strategies such as pro-social modelling,

the appropriate use of reinforcement and disapproval, and problem solving (Dowden & Andrews,

2004) as the most powerful influence strategies available. Programs that incorporate these

approaches when targeting criminogenic needs are more effective than those that do not

(Landenberger & Lipsey, 2005; Tong & Farrington, 2006). Techniques of motivational

interviewing (Miller & Rollnick, 1991), can also work to help engage youth in developing plans

for positive changes. It has been well established that CBT interventions work on a group level

but heterogeneity in outcomes implies that interventions are working for some youth but not

others (Van Voorhis, Spiropoulos, Ritchie, Seabrook, & Spruance, 2013). Factors such as


gender (Hubbard & Matthews, 2008; Vitopoulos, Peterson-Badali, & Skilling, 2012),

motivation (Serin & Kennedy, 1997), and race/ethnicity (Spiropoulos, Salisbury, & Van Voorhis,

2014; Usher & Stewart, 2003; Wilson, Lipsey & Soydan, 2003), cannot be ignored when

providing services to youth. Specific responsivity, sometimes referred to as the “fine-tuning” or

“tailoring” of cognitive behavioral interventions, describes taking into account the strengths,

learning style, personality, motivation, and bio-social (e.g., gender, race) characteristics of the

individual.

Implementation of the RNR Framework

Success of the RNR model rests upon two critical steps: a) reliable and valid assessment

of criminogenic needs, and b) implementation of services to address these needs (Peterson-

Badali et al., 2015), which equates to treatment ‘matching’ (i.e., providing evidence-based

services for individuals’ identified criminogenic needs while paying heed to their individually-

identified responsivity considerations). With respect to the first step, evidence indicates that

agencies adopting a validated RNR-based assessment systems demonstrate greater reductions in

recidivism and more appropriate allocation of resources (Andrews & Bonta, 2010; Vincent, Guy,

Gershenson & McCabe, 2012) that those that do not. The ‘fourth generation’ risk/need

assessment and case management tools currently in use include measures of risk, strengths,

needs, and responsivity as well as guidelines for re-assessment, service plans, service delivery,

and intermediate outcomes. However, with respect to the second step, studies indicate that the

needs of youth as identified in risk-need assessments are frequently not reflected in the services

they receive (Flores et al., 2004; Haas & DeTardo-Bora, 2009; Peterson-Badali et al., 2015;

Young et al., 2006). This ‘treatment gap’ can take the form of needs going unaddressed (Luong

& Wormith, 2011; Peterson-Badali et al., 2015), needs being over addressed (mental health and


substance use, Young et al., 2006), or probation officers simply not using assessments to

drive case management or services provided (Flores et al., 2004; Haas & DeTardo-Bora, 2009;

Miller & Maloney, 2013).

Barriers to implementation. Existing research suggests barriers to addressing matching

services to individually identified criminogenic need areas for a youth occur at multiple levels.

Traditional specific responsivity considerations are the most obvious and generally discussed of

these factors. For example, level of intellectual functioning is a responsivity factor often referred

to within the framework (Andrews and Bonta, 2010; Hubbard & Pealer, 2009; Keeling, Beech &

Rose, 2007). An example of addressing the needs of a youth with cognitive deficits might mean

matching programs to a youth’s conceptual level, which includes taking into account their

intelligence and problem solving skills, perhaps taking care to avoid program interventions with

a high need for abstract reasoning and self-reflection (Andrews & Bonta, 2010). Similarly,

matching services to a youth’s personality might require attention to qualities of extraversion,

impulsivity, and clinical diagnoses. Contrastingly, youths with anxiety problems would likely

not benefit from modes of treatment involving strong confrontational interpersonal exchanges,

making a group program less desirable and one-to-on counselling more appropriate (Andrews &

Bonta, 2010). Sociocultural variables like gender and ethnicity also fall under the umbrella of

specific responsivity. Although some studies have shown that the RNR framework applies

equally to males and females (Andrews & Bonta, 2010; Hubbard & Pratt, 2002; Rettinger &

Andrews, 2010; Olver, Stockdale & Wormith, 2009) and to people of different ethnicities

(Wilson, Lipsey & Soydan, 2003; Usher & Stewart, 2014), other treatment studies call for gender

specific (Vitopolous et al., 2010; Hubbard & Matthews, 2008; Van Voorhis, Wright, Salisbury,

& Bauman, 2010) and culturally sensitive (Spiropoulos et al., 2014; Van Voohris et al., 2013;


Wilson & Gutierrez, 2014) approaches to treatment. Responding to the vast heterogeneity

of dynamic client characteristics has the potential to be complex and exceedingly resource

intensive, leaving specific responsivity considerations the most under-prioritized of the RNR

framework (Nee, Ellis, Morris, & Wilson, 2013). This complexity begs the question of whether

challenges in addressing specific responsivity considerations, which requires individual tailoring

of programs, explains part of the low service-to-needs match for youth.

Related to the concept of responsivity, noncriminogenic needs that have the potential to

disrupt treatment have also been termed ‘destabilizers’ (Taxman, 2014). Taxman (2014, p. 35)

defines factors that relate to “lifestyle stability, decision-making, and the daily functioning of an

individual” as lifestyle destabilizers (e.g., unstable housing, financial insecurity, or living in a

high crime neighborhood) and clinical destabilizers (e.g., substance abuse or mental health

concerns). There is evidence implicating these variables as potential responsivity factors; for

example, Taxman & Pattavina (2013) found that individuals with four or more destabilizing

factors exhibited unhealthy daily functioning and were less likely to engage in or complete

treatment/supervision. However, there is no empirical evidence to suggest best practices for

addressing mental health (Vanderloo & Butters, 2012) or related destabilizing concepts as

responsivity factors. Furthermore, it is unclear at which point a noncriminogenic need becomes a

responsivity consideration and thus an appropriate target for correctional rehabilitation, making

confusion among frontline workers inevitable. Adding another potential layer to the

implementation puzzle, these destabilizing factors often become the focus of probation sessions,

as some probation officers believe it necessary to first stabilize youth by addressing these

lifestyle and clinical needs, before attending to criminogenic needs (Haqanee et al., 2015).


Often highlighted in research on youth crime is the role of families on the outcomes

of youth living in high-risk situations (Hirschi, 1969) as well as the importance of parental

involvement in youth justice processes (Broeking & Peterson-Badali, 2010; Davies & Davidson,

2001; Peterson-Badali & Broeking, 2010). Certain family variables have been identified as

criminogenic needs (e.g., parental supervision and monitoring) and it may also be the case that

some parental factors are relevant as potential barriers to service or destabilizing factors for

community sentenced youth. Parental factors such as involvement and cooperativeness in the

probation process have the potential to impact a youth’s ability to successfully engage in

treatment and also to reduce youths’ chances of recidivism (Burke, Mulvey, Schubert & Garbin,

2014; Henggeler & Sheidow, 2012; Maschi, Schwalbe & Ristow, 2013; Mendel, 2010). Lack of

parental involvement in the probation process may pose a structural barrier, as young people

may not have the support (e.g., transportation) necessary to attend appointments, as well as a

psychological barrier when parents fail to form a working partnership with the probation officer

or they express anti-treatment beliefs (Maschi et al., 2013). Furthermore, a problematic

relationship with parents may distract youth from engaging in treatment for criminogenic needs.

As such, parental factors cannot be ignored when examining the treatment gap.

Systems factors – such as long waitlists or a lack of available programming – also have

the very real potential to impact effective community supervision. The relatively new concept of

‘systemic responsivity’ describes how to reduce overall recidivism rates within a jurisdiction by

ensuring there are a sufficient number of programs available and that individuals have access to

these programs (Taxman, Pattavina & Caudy, 2014). Limited availability of resources in smaller

communities (Hannah-Moffat & Marutto, 2003) as well as long waitlists for outreach counseling,

mental health treatment, and intensive substance abuse treatment (Haqanee et al., 2015) suggest


that lack of programming may account for some of the disconnect between risk/need

assessment and good case management practice.

Lastly, probation officers’ evaluation of the complex combination of risk, needs,

responsivity, destabilizing, parental, and systems factors for each youth, requires them to make

day-to-day case management decisions that may serve to get some criminogenic needs matched,

while leaving others unaddressed. Research with probation officers indicates that at times they

prioritize working on a specific criminogenic need for which a client shows motivation, to the

exclusion of other identified needs, which they perceive will be an alternative to working on

nothing at all (Haqanee et al., 2015). Probation officers may also decide to work on certain ‘high

impact’ needs, that when successfully matched, also impacts other criminogenic need areas

(Haqanee et al., 2015). For example, targeting a youth’s education and/or employment needs

may provide youth with an opportunity to make appropriate use of their free time (i.e., leisure)

and become exposed to prosocial associates (i.e., peers). Such case management decisions may

explain the low service-to-needs match in specific domains.

The Current Study

The overall goal of the present study was to explore barriers to service provision in youth

serving community sentences. It is vital that such barriers be identified and reduced so that more

of young people’s assessed criminogenic needs are addressed. As such, the first objective of the

study was to describe the extent and nature of potential barriers to treatment by identifying and

categorizing issues that emerged in probation case notes in connection with youths not receiving

(appropriate) services. The second goal was to understand how the number and nature of these

potential barriers related to criminogenic risk as well as the nature of youths’ index offense. The

third goal was to examine whether these factors were related to youths’ receipt of services


(intervention and case management) – in other words, whether these ‘potential’ barriers

were ‘actual’ barriers to intervention. The final goal was to understand if barriers to treatment

predicted recidivism beyond the contributions of risk and the extent to which youth received

services addressing their criminogenic needs.

Method

Sample and Procedure

Data for the present study were obtained from the clinical files of 219 youth (174 males and

45 females) ranging in age from 12 to 20 years (M = 16.06, SD = 1.60) who underwent court-

ordered forensic assessments at a child and youth mental health program in Toronto, Canada

prior to their sentencing hearings. The sample was ethnically diverse: 43% Black, 22% White,

18% Asian, and 17% other. In terms of the index offense that prompted referral for assessment,

60% of youth were charged with violent non-sexual crimes (e.g., robbery, assault), 24% were

charged with non-violent crimes (e.g., theft, drug, and administrative offenses), and 16% were

charged with sexual offenses (e.g., sexual assault, invitation to touching).

Data were collected from clinical charts (including assessment reports based on

comprehensive, multi-source assessments conducted by one or more members of a

multidisciplinary team comprised of a psychologist, a psychiatrist, and social workers) and

probation case notes. Clinical charts were reviewed to obtain demographic information, offense

history, charges leading to the assessment, risk scores as well as evaluations of mental health,

cognitive, and academic functioning. Recommendations from the assessment reports were

reviewed to identify criminogenic needs. The extent to which youths’ identified criminogenic

needs were addressed via probation case management activities and intervention services

(termed ‘service-to-needs match’) was coded by reviewing youths’ probation files to determine


which recommendations were followed-up and what components of their sentences were

achieved. Probation case notes were also reviewed to identify barriers to service when a youth

did not receive services matched to their identified criminogenic needs.

Measures and Coding

Risk to reoffend and criminogenic needs. The Youth Level of Service/Case

Management Inventory (YLS/CMI; Hoge & Andrews, 2002, 2011) is a tool designed to assess

criminogenic needs, strengths, responsivity factors, and overall risk to reoffend in youths aged 12

to 18. The 42-item checklist is divided into eight categories: history of criminal conduct, family

circumstances and parenting, current school/employment functioning, peer affiliations, alcohol

and drug use, leisure and recreational activities, personality and behavior, and antisocial

attitudes, of which the latter seven sections are considered dynamic criminogenic needs domains.

Domain scores and categorical descriptors are calculated from summing individual items

(present, absent), and an overall risk score is calculated for each youth by summing all the items.

The psychometric properties of the YLS/CMI are adequate, with studies reporting strong

internal consistency for the total score (Rowe, 2002) and moderate to strong consistency for the

subscales (Rowe, 2002; Schmidt, Hoge & Robertson, 2002). Concurrent validity of the tool has

been demonstrated by the presence of significant correlations between YLS/CMI overall

risk/need scores and the Total Problem and Externalizing scores of Achenbach’s Child Behavior

Checklist and Youth Self Report measures (Schmidt et al., 2002; Skilling & Sorge, 2014).

Predictive validity has been demonstrated with significant correlations between scores on the

YLS/CMI and indexes of reoffending such as new charges, new convictions, and charges for

serious offenses (Costigan & Rawana, 1999; Jung & Rawana, 1999; Peterson-Badali et al., 2015;

Rowe, 2002; Schmidt et al., 2002; Vieira, Skilling, Peterson-Badali, 2009; Vitopolous et al.,


2012). Interrater reliabilities for the YLS/CMI total score completed independently by two

primary clinicians for a subsample of youth used in the present study, as measured by Pearson

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

Service-to-needs match. For each of the seven dynamic criminogenic need domains,

assessment reports were reviewed to determine whether clinicians identified some aspect of the

domain as requiring intervention and/or case management (1) or not (0)2. For each domain

identified as a ‘1’, probation case notes were reviewed to establish if service was received and, if

so, whether it was sufficiently consistent with the clinician recommendation in terms of both

quality and quantity. A need was considered matched (1) when probation records indicated that a

youth consistently attended an evidence-based program. A match was considered absent (0)

when an identified need was not at all mentioned in probation case notes, programming received

was not evidence based, treatment intensity was low when high intensity was recommended, or

when youth rarely attended sessions. For example, a match was coded as ‘yes’ (1) if a

recommendation was made in the family domain and the youth and her/his family completed a

12-session long (quantity), evidence-based (quality) family-counseling program. Other examples

of service-to-need match included attendance in a school program following a recommendation

in the education domain or obtaining employment/participation in an employment training

program following a recommendation in the employment domain (see Peterson-Badali et al.

(2015) and Vitopoulos et al. (2012) for a detailed description and examples of service-to-needs

match coding). Interrater reliability for coding of service matching was very strong, (Peterson-

Badali et al., 2015; Vitopolous et al., 2012).

2 Court-ordered report recommendations were used rather than YLS/CMI scores in order to reflect real-world case management. Probation officers in Ontario generally conduct their own risk/need assessments. However, the court-ordered report gave them a unique, in-depth source of information providing them with high quality material used to guide case management.


An ‘overall match’ score for each youth was calculated by dividing the number of

domains that were matched with appropriate service by the total number of identified need

domains; proportion scores ranged from 0 to 1. Scores were also divided into High Match (youth

who received services for at least half of their identified criminogenic needs, i.e., proportion

scores ranging from .5 to 1) and Low Match (youth who received services for less than half of

their identified criminogenic needs, i.e., proportion scores ranging from 0 to .49).

Potential Barriers to Service. Based on case note review, Potential Barriers were

defined as factors that appeared to impede service-to-need match in unmatched domains; these

were coded as present or absent. For domains that had a clinician recommendation but was coded

as unmatched, case notes were scanned for explicit reference to barriers to service (e.g.,

indications that a youth was placed on a long waitlist and did not receive programming in a

timely manner). Case notes were also reviewed holistically to determine barriers to service not

explicitly stated. Case notes were independently coded by two raters for 20% of files; calculated

using Cohen’s Kappa, agreements ranged from .63 to 1.0 across categories, with a mean of .84).

Recidivism. Data from a national police criminal database was obtained to determine if

youth reoffended within an approximate 3-year follow-up period after the conviction that caused

them to be referred for the court-ordered report. Reoffenses were included only for new

convictions, after the assessments were complete and after the youth was sentenced. Reoffenses

that occurred within six months after the assessment date were not counted in order to give

sufficient time for probation services to engage with youth.


Results

Preliminary Analyses

Table 1 provides information on youths’ demographic characteristics, offense category,

risk scores, and recidivism patterns by match level

Table 1 Demographic and Criminal History Characteristics by Criminogenic Match Category Variable

Low Match n = 144

High Match n = 75

Total N = 219

Comparison

Age (years) 16.16 15.89 16.06 t = 1.17, p = .24 % Gender (n) Male 81.9 (118) 74.6 (56) 79.5 (174) χ2 (1) = .1.60, p = .21 Female 18.1 (26) 22.6 (21) 25.3 (19) % Ethnicity (n) Black 42.6 (26) 43.6 (17) 43.0 (43) χ2 (3) = .32, p = .96 White 21.3 (13) 23.1(9) 22.0 (22) Asian 19.7 (12) 15.1(6) 18.0 (18) Other 16.4 (10) 17.9 (7) 17.0 (17) YLS/CMI Mean Scores Total Risk 21.06 17.52 19.84 t = 2.76, p = .01 Criminal History 2.11 1.17 1.77 t = 3.80 , p < .01 Family 3.39 3.15 3.31 t = .93, p = .35 Education 4.08 3.46 3.86 t = 2.23, p = .03 Substance Use 1.95 1.49 1.78 t = 1.83, p = .07 Personality 3.63 3.25 3.50 t = 1.22, p = .23 Attitude 2.04 1.59 1.88 t = 1.81 , p = .07 Leisure 1.74 1.42 1.63 t = 2.28 , p = .02 Peers 2.39 2.01 2.26 t = 2.01 , p = .05 % Index Offense Non-violent 29.0 (40) 13.9 (10) 23.8 (50) χ2 (2) = 7.86, p = .02 Violent 58.0 (80) 62.5 (45) 59.6 (125) Sexual 13.0 (18) 23.6 (17) 16.7 (35) % Recidivism (n) Yes (65.9) 83 (41.9) 39 (55.7) 122 χ2 (1) = 12.43, p < .01 No (34.1) 43 (58.1) 54 (43.8) 96 Days to Reoffend 757 961 827 t = -3.37, p < .01 a Valid percentages are reported where data was unavailable


Criminogenic need match did not vary according to age, gender, or ethnicity. However,

youth with low match had higher total YLS/CMI risk scores as well as higher scores in the

criminal history, education and leisure domains. Groups also differed by category of offense,

recidivism, and days to reoffend. The percentage of violent offenders was comparable in the low

and high match groups; however, the low match group had more non-violent offenders, while the

high match group had more youth charged with sexual offenses. Youth with low match were also

more likely to reoffend, and to do so within a shorter period of time, than high match youth.

Question 1: What are potential barriers to treatment for community sentenced youth?

Thirty-eight3 variables were initially coded as potential barriers to service, including

responsivity considerations, aspects of criminogenic needs, lifestyle variables, parental issues,

systems variables, and case management decisions. Variables that were not present in at least

10% of cases were excluded from the final set. The remaining 19 variables were assigned to one

of seven composites, defined below. Table 2 displays the percentage of youth with each barrier

and the percentage of youth with one or more barrier in each composite.

Lifestyle destabilizers. Factors were defined as having an impact on the lifestyle

stability and overall functioning of an individual (Taxman, 2014). Youth going AWOL was

coded as a barrier to treatment when case notes indicated that youth was missing and this

absence interfered with the probation officer’s ability to provide treatment for criminogenic need

areas. Unstable living conditions were flagged when the youth was homeless, living in a

3 Transportation issues; mental health concerns; youth going AWOL; unstable living conditions; financial issues; poor motivation; substance abuse; abuse, neglect or trauma; learning problems; low intelligence; denial; manipulation; poor social skills; poor problem solving skills; ethnic or cultural barriers; immaturity; gang affiliation; high crime neighborhood; parental beliefs; parental criminality; parental substance abuse; parental mental health; no parental involvement in probation; conflictual relationship with parents; long waitlist; lack of programs; pushback from the community; PO worked on need youth was motivated to work on; PO prioritized certain needs; not enough time in the index period; bail conditions; insufficient educational supports


Table 2 Composites and Barriers to Treatment

Composites/Barrier % Frequency (n)

Lifestyle Destabilizers 50.2 (110) AWOL 15.1 (33) Unstable living conditions 30.1 (66) Financial issues 26.0 (57) High crime neighbourhood

13.7 (30)

Clinical Destabilizers 44.3 (97) Mental health concerns 20.5 (45) Substance abuse 25.1 (55) Abuse, neglect or trauma

20.1 (44)

Capacity Issues 33.3 (73) Learning problems 21.0 (46) Low intelligence 16.4 (36) Immaturity

17.8 (39)

Engagement Issues 52.5 (115) Poor motivation 47.5 (104) Denial

25.6 (56)

Parental Factors 62.6 (137) Parental Beliefs 20.1 (44) Conflictual relationship with parent(s) 31.1 (68) No parental involvement in probation

34.2 (75)

Systems Factors 35.6 (78) Long waitlist 15.5 (34) Lack of programs

26.0 (57)

Case Management Desicionsa 59.4 (130) PO worked on need youth was motivated to work on 26.0 (57) PO prioritized needs 51.1 (112)


shelter, couch surfing, moving addresses, or transferring institutions so much that the

subject of probation appointments centered around housing and/or find the youth a place to live

at the expense of criminogenic needs. Financial issues were relevant when they were the main or

a frequently referred to subject of probation appointments (e.g., applying for student welfare or

obtaining a job to support a single parent family). High crime neighborhood applied when there

were explicit concerns from the youth, probation officer, parent, and/or another worker about the

youth residing in a certain area due to violence, gang activity, or drug use.

Clinical destabilizers. This category consisted of non-criminogenic DSM-IV diagnoses

(e.g., depression, anxiety, PTSD, psychotic disorders) or subclinical levels of such diagnoses that

interfered with treatment. Diagnoses not included in this category were Conduct Disorder,

ADHD, Oppositional Defiant Disorder, Substance Abuse Disorder, and Learning Disability.

Substance abuse was only coded as a barrier to treatment when a youth’s lifestyle centered on the

getting and using of substances such that his or her ability to make decisions about treatment and

probation (i.e., attendance, participation) became unreliable and sporadic. It is important to note

here that not all youth who had substance use flagged as a criminogenic need domain had

substance use coded as a barrier. Similarly, for a number of youth, substance use was not

identified as a criminogenic need but did emerge as a barrier to service in other identified need

domains. In addition, any reference to abuse, neglect or trauma during the index period was

included in this composite. Historical episodes of a severe nature also warranted this factor to be

coded as present.

Capacity issues. This category included learning problems, low intelligence, and

immaturity; these were coded as present when explicitly stated by probation officers, other

informants, or in assessment reports. ‘Learning problems’ was always coded when the youth


was identified as having low intelligence but also when a learning disability or ADHD

interfered with their ability to receive treatment in any domain, however a diagnosis of LD or

ADHD alone was never enough to warrant the coding of this barrier. Immaturity was also coded

when youth showed poor impulse control, poor planning, and concrete-oriented thinking.

Engagement issues. Poor motivation was coded as present when youth would not

participate in treatment. This factor was coded when explicitly stated by the probation officer

and also when the youth displayed behavior indicating poor motivation (i.e., not following

through with treatment, appointments, phone calls, etc.). Denial was coded when case notes

indicated that youth did not think he/she had a problem, and this caused limited participation in

treatment.

Parental factors. Parental beliefs were coded when parents expressed beliefs that may

have had an impact on how youth engaged in treatment. An example of this would be if parents

expressed anti-treatment sentiments or if they believed that the probation process was unjust and

their child was being unfairly victimized by the system. A conflictual relationship applied as a

barrier when issues were current and distracting for the youth. No parental involvement

pertained to when parents refused to participate (also coded under beliefs), were too busy, or any

other reason they were not able to directly engage with the probation officer. This was also

coded if parents were not able to indirectly participate by providing their child with the support

(i.e., transportation, structure) to be successful.

Systems factors. Long waitlist applied when services were backlogged or youth had to

wait more than a month to get into a program. Sometimes poor communication between

probation services and certain programs resulted in a delay. Oftentimes, treatment was set up but

did not materialize during the index period. ‘Lack of programs’ was coded when there were no


programs available. This barrier was determined as being present when the reason youth

was not provided with treatment was not a probation officer factor, a youth factor, or a parental

factor, but a factor that reflected a systems level deficit.

Case management decisions. This two-item composite referred to partial barriers that

applied only to some criminogenic need domains for a particular youth. When a youth was

motivated to work on some needs but not others, ‘PO worked on need youth was motivated to

work on’ was coded as present, for those needs that were not matched. ‘PO prioritized certain

needs’ was coded when it appeared the probation officer did not provide services because she

judged that the youth was doing well (i.e., displaying prosocial behavior, not reoffending). In

other cases this barrier was coded because it appeared that the probation officer prioritized

certain high impact needs at the expense of others, as a youth who lacked motivation would

likely not engage in treatment for multiple needs.

Barrier composites. Examining the seven barrier composites and their relationships to

each other as well as criminal history and overall match at the bivariate level revealed significant

relationships (see Table 3). As expected, all barrier composite scores had a negative relationship

to overall match, with significant effects for lifestyle destabilizers, engagement issues, parental

issues, and when probation officers prioritized some needs over others. Youth with higher

criminal history scores had more lifestyle and clinical destabilizing factors as well as more

engagement issues. Barriers were at the most, moderately related to each other, making them

relatively independent predictors in subsequent regression models.


Table 3 Bivariate Correlations Between Barrier Composites, Overall Match And Criminal History

O

vera

ll M

atch

Y

LS C

rimin

al H

isto

ry

Li

fest

yle

Des

tabi

lizer

s

C

linic

al D

esta

biliz

ers

C

apac

ity Is

sues

En

gage

men

t Iss

ues

Pa

rent

al F

acto

rs

Sy

stem

s Fac

tors

Cas

e M

anag

emen

t

Overall Match − YLS Criminal History -.30** −

Lifestyle Destabilizers -.21** .39** −

Clinical Destabilizers -.16* .25** .40** −

Capacity Issues -.11 .05 .02 .15* − Engagement Issues -.42** .34** .41** .31** .15* −

Parental Factors -.37** .13 .23** .30** .21** .29** −

Systems Factors -.13 .02 -.14* -.05 .004 .03 .08 − Case Management -.14* .07 -.02 -.07 .04 .19** .09 .08 − *significant at the .05 level;,**significant at the .01 level

Question 2: How does a youth’s total number of barriers relate to their crime, risk level,

and recidivism outcomes?

Tallied across composites, the average number of barriers for each youth was 4.83 (SD =

2.81, range = 0-12). In order to assess the extent to which barriers were related to risk score and

recidivism it was necessary to compute a non-criminogenic total barrier score so as not to

include any potential criminogenic needs. This non-criminogenic need barrier score was

calculated for each youth by removing barriers that overlapped with criminogenic needs:

substance abuse, learning problems, immaturity, poor motivation, and conflictual relationship


with parent(s). Youth had an average of 3.41 non-criminogenic barriers (SD = 1.96, range

= 0-8). Number of barriers, both the inclusive (i.e., barrier score that includes criminogenic and

non-criminogenic need barriers) and non-criminogenic variety, did not differ significantly across

age, gender, ethnicity, or days to reoffend (for the subsample of recidivists). As shown in Table

4, both variables varied across category of offense, YLS/CMI risk category (i.e., low, moderate,

high, and very high), as well as recidivism (yes or no).

For significant differences, total barrier categories (both the inclusive and

noncriminogenic type) did not differ in pattern. Youth whose index offense was sexual in nature

had significantly less barriers to treatment than youth who offended violently, and these groups

Table 4 Mean Number of Barriers per Youth Based on Inclusive and Non-criminogenic Categories for Category of Offense, Risk Level, and Recidivism Outcomes

Variable Mean # of

Barriers (SD)

Inclusive Total Barriers

Non-Criminogenic Total Barriers

Category of Offense

F(2,207) = 5.77, p = .004, η2 = .05,

F(2,207) = 4.53, p = .01, η2 = .04

Violent 5.21 (2.95) 3.65 (1.98) Non-violent 4.88 (2.41) 3.42 (1.80) Sexual

3.43 (2.46)

2.54 (1.86)

YLS/CMI Risk Level

F(3,215) = 27.85, p < .001, η2 = .25

F(3,215) = 23.91, p < .001, η2 = .25

Low 2.26 (2.34) 1.71 (1.73) Moderate 4.30 (2.36) 3.02 (1.68) High 5.78 (2.43) 4.08 (1.68) Very High

7.27 (2.63)

5.00 (1.72)

Recidivism t(217) = -5.86, p <.001, d = .79 t(217) = -5.49, p <.001, d = .75 Yes 3.67 (2.74) 2.64 (1.87) No

5.75 (2.51)

4.02 (1.82)

Days To Reoffend

r(97) = .003, p = .98 r(97) = .01, p = .92


did not differ significantly from youth who offended non-violently. The distribution of risk

level based on the YLS/CMI categories among youth in the sample was variable: 16% of youth

were low risk to reoffend, 39% were moderate risk, 33% were high risk, and 12% were very high

risk. For both variables, low risk youth had the least number of barriers to treatment, followed by

the moderate risk group, the high risk group, and the very high risk group; pairwise differences

were all significant except between the high and very high risk groups. Youth who reoffended

also had significantly more barriers to treatment than those who did not. Effect sizes for the

relationship between both total barriers variables and risk level as well as recidivism were strong.

Question 3: To what extent are barriers to treatment associated with criminogenic need

match?

To investigate the contribution, beyond static risk, of barriers to treatment match in each

domain, seven logistic regressions were conducted on which ‘treatment match’ in each domain

was regressed on youths’ ‘criminal history’ score from the YLS/CMI4, at Step 1, and seven

barrier composites at Step 2, in a hierarchical logistic regression analysis, as shown in tables in

appendices A through H.

Family. In the family domain (n = 181), the barrier ‘conflictual relationship with

parents(s)’, was removed from the analysis due to overlap with YLS/CMI items of this domain.

The regression model was significant at both steps, Step 1 model, χ2(2) = 12.23, p < .001, Step 2,

χ2(8) = 43.42, p < .001, with criminal history acting as a significant predictor at Step 1, Wald χ2

4 Criminal History was chosen in lieu of the total YLS/CMI risk–need score because the outcome variable for the logistic regression (i.e., binary match level) is in part derived from this score. Clinician recommendations were based on YLS-identified criminogenic needs; thus, there is overlap between the dynamic domains of risk–need, recommendations, and subsequent match scores. However, since Criminal History and YLS/CMI risk scores were highly correlated for our sample, r(218) = .66, p < .001, thus variable is a strong proxy for risk.


= 11.08, p = .001, OR = .72, 95% CI = [.60, .87], and Step 2, Wald χ2 = 9.27, p = .002, OR

= .69, 95% CI = [.54, .88]. At Step 2, parental factors, Wald χ2 = 17.20, p < .001, OR = .22, 95%

CI = [.11, .45] also contributed. For each point increase on the YLS criminal history score,

youth were 31% less likely to be matched; for every addition of a parental barrier, youth were

78% less likely to be matched in this domain.

Education. In the education domain (n = 201), the model was significant at both steps,

Step 1 Model χ2(1) = 14.80 p < .001; Step 2 Model χ2(8) = 46.03, p < .001, with criminal history

contributing at Step 1, Wald χ2 = 13.77, p < .001, OR = .72, 95% CI = [.61, .86] and engagement

issues contributing at Step 2, Wald χ2 = 10.59, p = .001, OR = .46, 95% CI = [.28, .73]. Youth

with poor motivation, denial, or both, were 54% less likely to be matched for services in this

domain, over and above the effect of their criminal history.

Employment. In the employment domain (n = 104), the model was significant at Step 2,

χ2(8) = 27.61, p = .001, with case management decisions acting as a significant predictor, Wald

χ2 = 9.12, p =.003, OR = 3.26, 95% CI = [1.51, 7.00]. Youth whose probation officers

prioritized certain needs over others were over three times more likely to get matched in the

employment domain than those whose probation officers did not. Systems factors also emerged

as a significant predictor, Wald χ2 = 3.94, p =.05, OR = .43, 95% CI = [.18, .99]. With every

addition of a systems barrier, youth were 57% less likely to receive services geared towards their

employment needs.

Personality. In the personality domain (n = 177), the model was significant at both steps,

Step 1 Model χ2(1) = 8.05, p = .01; Step 2 Model χ2(8) = 25.97, p = .001 , with criminal history

only contributing at Step 1, Wald χ2 = 7.60, p =.01, OR = .77, 95% CI = [.64, .93]. Engagement

issues, Wald χ2 = 5.97, p = .02, OR = .54, 95% CI = [.33, .89] contributed at Step 2, over and


above static risk. Youth were 46% less likely to receive treatment for their personality

needs when they had more engagement barriers.

Leisure. In the leisure domain (n = 163), the model was significant at both steps, Step 1

Model χ2(1) = 7.05, p = .01; Step 2 Model χ2(8) = 50.65, p < .001, with criminal history only

contributing at Step 1, Wald χ2 = 6.41, p =.01, OR = .75, 95% CI = [.60, .94]. Engagement

issues, Wald χ2 = 13.58, p < .001, OR = .20, 95% CI = [.08, .47], systems factors, Wald χ2 =

4.93, p =.03, OR = .39, 95% CI = [.17, .89], and case management decisions, Wald χ2 = 4.20, p

=.04, OR = .51, 95% CI = [.27, .97], contributed at Step 2. Youth with engagement issues were

80% less likely to receive treatment in this domain. Furthermore, youth who encountered long

waitlists, lack of programs, or some combination of these factors, were 61% less likely to be

receive treatment in the leisure domain despite their overall risk score. Finally, youth whose

probation officers prioritized some needs over others were 49% less likely to get the leisure

needs matched, suggesting that the leisure domain was given less priority in order to address

other needs.

Peers. In the peer domain (n = 181), the model was significant at both steps, Step 1

Model χ2(1) = 7.97, p = .01; Step 2 Model χ2(8) = 36.04, p < .001, criminal history only

contributing at Step 1, Wald χ2 = 6.94, p =.01, OR = .71, 95% CI = [.56, .92]. Parental factors,

Wald χ2 = 5.08, p =.02, OR = .42, 95% CI = [.19, .89], and systems factors, Wald χ2 = 7.47, p

=.01, OR = .26, 95% CI = [.10, .68], contributed at Step 2, over and above criminal history.

Youth with who encountered these barriers were less likely (58% for each additional parental

barrier and 74% for each additional systems barrier) to receive treatment in the peer domain.

Attitude and substance use. In the attitude domain (n = 117), the barrier composite

‘engagement issues’, and in the substance use domain (n = 123) the ‘substance use’ barrier was


removed from the analyses due to overlap with YLS/CMI items of this domain. Neither of

these domains yielded significant results, possibly due to the low frequency of matched youth

(19.7% in the attitude domain and 18.7% in the substance use domain) generating highly

significant models at Step 0.

Overall match. To ascertain whether the various types of barriers impeded youth from

receiving services for their criminogenic needs overall, overall match (the proportion match

variable), was regressed on criminal history at Step 1, and seven barrier composites at Step 2, in

a hierarchical linear regression. Bootstrapping, a method for deriving robust estimates for

parametric tests when assumptions are in doubt, based on 1000 samples, was applied, as 32.9%

of the sample had none of their identified needs matched. As shown in Table 5, the model was

significant at both steps, Step 1 Model F(1, 198) = 19.33, p < .001, R2 = .09, Step 2 Model,

F(8,198) = 11.21, p < .001, R2 = .32. In the Step 1 model, criminal history accounted for 9.0%

of the variance in overall match and at Step 2, 23% of variance was added by barrier composites;

this change was significant F (7,190) = 9.24, p < .001.

Table 5 Summary of Hierarchal Regression Analysis – Overall Match Predicted by Criminal History and Seven Barrier Composites (n = 219) Model 1 Model 2 Variable B SE B βa B SE B βa Constant 0.44 0.03** 0.66 0.05** YLS Criminal History -0.05 0.01 -.30*** -0.03 0.01 -0.14* Lifestyle Destabilizers -0.02 0.03 -0.05 Clinical Destabilizers 0.01 0.03 0.01 Capacity Issues -0.03 0.02 -0.08 Engagement Issues -0.12 0.03 -0.29** Parental Factors -0.10 0.03 -0.23*** Systems Factors -0.08 0.03 -0.15* Case Management -0.05 0.03 -0.11 R2 .09 .32 F for change in R2 19.33*** 11.21***

ap values based on bootstrap results run on 1000 bootstrap samples; * p < .05; ** p < .01; *** p < .001


Criminal history was a significant predictor at Step 1, β = -.30, p < .001, 95% CI =

[-.08, -.03], and Step 2, β = -.14, p =.05, 95% CI = [-.05, -.002]. At Step 2, engagement issues, β

= -.29, p = .002, 95% CI = [-.18, -.06], parental factors, β = -.23, p = .001, 95% CI = [-.15, -.04],

and systems factors, β = -.15, p = .02, 95% CI = [-.14, -.01], also emerged as significant

predictors of overall treatment match. Youth with more extensive criminal histories and more of

these issues had a lower proportion of their identified criminogenic needs addressed during the

course of their community supervision periods.

Question 4: Do barriers to treatment account for recidivism beyond static risk and overall

treatment match?

To examine the contribution of barrier composites on recidivism beyond criminal history

and criminogenic need match, recidivism (yes, no) was regressed on criminal history (Step 1)

and overall match (Step 2), as well as seven barrier composites (Step 3), in a hierarchical logistic

regression analysis; bootstrapping was applied. As shown in Table 6, the model was significant

at all three steps. Criminal history was a significant predictor of recidivism at all three steps of

the model, revealing at Step 3 that for every point increase on the YLS/CMI criminal history

score, youth were 37% more likely to reoffend. Overall match was significant at Step 2 but not at

Step 3 when barriers to treatment were added to the model. Youth were more likely to reoffend

when they had more lifestyle destabilizers (73% more likely for each additional destabilizer) and

when they had more capacity issues (60% more likely for each additional capacity issue),

regardless of the overall proportion of their criminogenic needs addressed during probation.

Figure 1 provides a brief summary of all significant regression findings.


Table 6 Hierarchal Logistic Regression – Recidivism Predicted by Criminal History, Overall Criminogenic Need Match, and Seven Barrier Composites (n = 219) CI (95%) Model Variables β SE β Wald’

s χ2 df pa Exp

(B) Lower Upper

Model (Step) 1 YLS Criminal History 0.42 0.11 16.35 1 < .001 1.53 1.24 1.88 Constant -0.28 0.24 1.32 1 .25 .76 Overall model at Step 1 18.91 1 < .001

Model (Step) 2 YLS Criminal History 0.33 0.11 9.19 1 .002 1.40 1.13 1.73 Overall Need Match -1.98 0.63 10.01 1 .002 0.14 0.04 0.47 Constant 0.56 0.36 2.43 1 .12 1.76 Step 2 10.71 1 .001 Overall model at Step 2 29.62 2 < .001

Model (Step) 3 YLS Criminal History 0.31 0.12 6.67 1 .01 1.37 1.08 1.73 Overall Need Match -1.42 0.76 3.52 1 .06 0.24 0.05 1.07 Lifestyle Destabilizers 0.55 0.25 4.80 1 .03 1.73 1.06 2.84 Clinical Destabilizers -0.38 0.27 2.10 1 .15 0.68 0.41 1.15 Capacity Issues 0.47 0.22 4.67 1 .03 1.60 1.04 2.44 Engagement Issues 0.08 0.29 0.07 1 .79 1.08 0.61 1.90 Parental Factors 0.02 0.27 0.01 1 .93 1.02 0.61 1.72 Systems Factors 0.56 0.34 2.66 1 .10 1 0.89 3.40 Case Management -0.09 0.27 0.10 1 .75 0.92 0.55 1.54 Constant -0.29 0.69 0.18 1 .68 0.75 Step 3 11.42 7 .12 Overall model at Step 3 41.04 9 < .001

Discussion

One of the aims of RNR framework is to provide case managers with clear guidance for

the challenging task of providing community supervision to a heterogeneous population of

youth, each with a unique set of risk factors, individual need areas, as well as personal

characteristics and circumstances that are constantly changing. Understanding the way this

process is being carried out on the ground and the factors impeding effective implementation is

important to aid efforts to form a synthesis between science and practice.


Engagement Issues

Education OR = .46

Personality OR = .54

Leisure OR = .20

Overall Match β = -.29

Systems Factors

Employment OR = .43

Leisure OR = .39

Peer OR = .26


Parental Factors

Family OR = .35

Peer OR = .42


Case Management

Desicions

Employment OR = 3.26

Leisure OR = .51

Lifestyle Destablizers

Recidivism OR = 1.73

Capacity Issues

Recidivism OR = 1.60

Figure 1. Summary of significant regression findings by barrier category; odds ratios and beta values present where applicable


Understanding Barriers to Treatment

The significant gap between youths’ identified criminogenic needs and services received

makes the study of barriers to treatment an important area of research. Identifying barriers to

treatment in the present study involved taking a comprehensive approach to examining why

services were not received, while openly acknowledging that some of these barriers were

a) related to one another, b) criminogenic in nature and c) outside the boundaries of the RNR

framework, but still important to report. Barriers in this sample were widespread and numerous;

ninety percent of youth in had at least one barrier to treatment and over half of youth had five or

more barriers. Consistent with existing research, the broad categories of lifestyle destabilizers

(Taxman & Caudy, 2015), engagement issues (Cohen & Whetzel, 2014), parental factors

(Maschi et al., 2013), and probation officers’ prioritization of certain needs (Haqanee et al.,

2015) were present for over half of the sample.

The interrelation of barriers. An interesting finding that emerged was that barrier

categories were not independent from one another. Although the retrospective nature of the

study makes inferences of causality strictly theoretical, one way to conceptualize the

relationships between barriers is sequentially (i.e., barriers as primary, secondary, tertiary, etc.),

with some existing as the result of others, creating unique pathways of intermediary goals for

different youth. For example, engagement issues were significantly related to all barrier

categories at the bivariate level (with the exception of systems factors) in a way that suggested

that motivation and/or denial may have been the by-product of other circumstances for some

youth. Understanding this sequence is important, as a youth whose engagement issues are

secondary to a particular primary barrier (e.g., low cognitive functioning) may need a different

kind of intervention than a youth whose motivation is low because of a factor such as unstable


housing. Furthermore, if addressing barriers to treatment is a necessary prelude to

addressing criminogenic needs, deciding at which point to intervene becomes a matter of

question – the beginning of the barrier chain, the subtle and hard to define point at which barriers

become responsivity considerations, or the point at which barriers become criminogenic?

Alternatively, another way to look at this interrelation of barriers is to consider them

simultaneously; barriers to treatment, like criminogenic needs, may cluster together (Taxman &

Caudy, 2015), possibly making barrier or responsivity patterns more important than individual

factors; this is an area for future research.

Criminogenic barriers. The criminogenic nature of some barriers to treatment also

warrants discussion. It is tempting to lump barriers neatly into the third (responsivity) principle

of the framework, as barriers and responsivity considerations share in common that they provide

supplementary treatment goals that are not necessarily the main focus of correctional treatment.

However, overlap exists between some barriers to treatment and aspects of criminogenic need

domains, suggesting that for some youth, some criminogenic needs may act as a ‘blockage’ to

treatment for other needs, making a neat classification of barriers under one principle of the

framework impossible. This also provided a methodological challenge, as including

criminogenic-type barriers in analyses for criminogenic treatment match ran the risk of

producing tautological results.

Conceptually, barriers to treatment provide supplementary treatment goals for youth on

community supervision, as they are factors that predict whether and how treatment occurs, while

criminogenic needs provide primary treatment goals because they are the factors that predict

reoffending. In the present study, substance use, poor motivation, and a conflictual relationship

with parent(s) were unique barriers in that they were directly linked to offending but also had the


potential to ‘unblock’ access to treatment for other criminogenic needs. As such, instead

of ignoring these factors and only examining non-criminogenic barriers, criminogenic-type

barriers were removed as predictors in analyses for domains in which the overlap would make

significant results circular (i.e., engagement issues as a predictor in the antisocial attitude

domain). Including barriers in analyses where the outcome was treatment match in domains for

which there was little or no overlap (e.g., engagement issues as a predictor in the leisure

domain), allowed for an examination of which aspects of criminogenic needs were also barriers

to treatment.

Systems level. To complete the puzzle, low service-to-needs match was also approached

from a systems level. The presence of systems barriers (i.e., long wait lists and lack of

programs) maps onto the concept of systemic responsivity (Taxman, 2014), indicating the need

for additional programming to address the complex array of offender needs and characteristics in

a jurisdiction. Case management decisions or ‘partial barriers to treatment’ reflect existing

research that explains probation officer strategies (Haqanee et al., 2015). Research has shown

when too many criminogenic needs are targeted in a single session, it can lead to making it

difficult to adequately address them all, and ultimately higher recidivism rates (Bonta et al.,

2008). As such, choosing to address one need at the expense of another, especially one that is

more causally related to recidivism (i.e., part of the ‘Big Four’), may prove to be a wise strategy.

Barriers to Treatment and Risk Level

Findings related to the first principle of the RNR framework revealed that increases in

risk level resulted in multifaceted challenges to treatment. Higher risk youth had lower treatment

match than their lower-risk counterparts. This finding is concerning considering that the Risk

Principle of the RNR framework stresses the importance of providing treatment to higher risk


youth. The greater number of criminogenic needs and/or the greater dosage of treatment

intensity required for high-risk youth inherently makes treatment success and completion more

challenging. However, in addition to more criminogenic needs, high and very high risk youth

were also characterized by more barriers to treatment, specifically, higher frequencies of lifestyle

destabilizing, clinical destabilizing, systems, and engagement barriers compared to their lower-

risk peers, making these youth more complex overall, not just ‘higher risk’. Some researchers

have pointed out that the failure to understand the complex interactions of the “three-legged

stool” – risk, criminogenic needs, and destabilizers (Taxman & Caudy, 2015) – has posed a

major barrier in the field of corrections. It seems this would especially be the case for these high

risk, more complex youth who may require the incorporation of their noncriminogenic needs in

case management, that might be serving as barriers to treatment. Low match may also reflect a

certain level of pushback from the system when dealing with these youth. For example, youths

who get a bad reputation may not be accepted into certain treatment programs or schools

(Sander, Sharkey, Groomes, Krumholz, Walker & Hsu, 2011). In addition, there seemed to have

been a limited number of programs designed to deal with those in the pre-contemplative stage of

change or with antisocial attitudes (Haqanee et al., 2015).

Next, to understand the low service-to-needs match, differences between low and high

matched youth as well as youth with different numbers of barriers to treatment were examined.

Youth whose index offenses were sexual in nature had fewer barriers to treatment than violent

offenders, were matched to services more often than non-violent offenders, had less

criminogenic needs and therefore were mostly deemed low and moderate risk. Previous research

suggests that these youth possess unique developmental risk factors, sometimes making them

low-risk to reoffend and responsive to treatment (Wanklyn, Ward, Cormier, Day & Newman,


2012; Worling & Curwen, 2000). Low frequency of recidivism among this group (Van

Wijk, Mali, Bullens & Vermeiren, 2007) might reflect low risk, less criminogenic needs,

decreased personal and parental barriers but it may also reflect a treatment effect, whereby

specialty programs have been designed to address the specific needs of this population and, as

such, they encounter fewer systems barriers such as long waitlists or a lack of programming.

Barriers to Treatment and Criminogenic Need Match

Some categories of barriers had broader effects than others, in that they predicted the

likelihood of not receiving treatment in more criminogenic need domains. Problems with

engagement was the most commonly-associated barrier to service match across domains,

impacting treatment receipt in education, leisure, and personality as well as overall proportion of

needs met. Research has shown that level of motivation often defines how well individuals fare

in treatment (Stewart & Millson, 1995; Thorton & Hogue, 1993). Case managers must address

obstacles of minimization, denial, and rationalization common among offenders (Kennedy,

2000), making sure that intervention is consistent with the individual’s stage in the change

process (Prochaska & DiClemente, 1986). Motivational interviewing (MI) is a collaborative,

person-centered form of guiding clients in an environment that increases treatment engagement

through the implementation of skills, techniques, and the MI spirit (Miller & Rollnick, 1991).

This environment requires case managers to take on a counselling role, or that of a ‘change

agent’, a role that some researchers argue is associated with less client resistance, increased

motivation, cooperation, and compliance (Bourgon, Gutierrez & Ashton, 2012; Skeem, Louden,

Polaschek, & Camp, 2007).

As mentioned before, engagement issues were significantly correlated at the bivariate

level with other categories of barriers, the highest correlation being lifestyle destabilizers.


Correlations with other barriers suggest that youths’ engagement issues may reflect

interrelated, longstanding, and entrenched family and community-level issues that some

probation officers cite as impeding criminogenic need treatment (Haqanee et al., 2015).

Research has shown that some probation officers report a lack of self-efficacy addressing youths’

thinking and attitudes in the presence of lifestyle destabilizing factors, a level of need they do not

feel equipped to deal with (Haqanee et al., 2015). The working alliance has recently been

referred to as a good proxy for treatment engagement (Bourgon & Bonta, 2014). Training with

more formal means to identify and monitor change in clients’ motivation with programs like

STICS (Strategic Training Initiative in Community Supervision; see Bonta et al., 2011), may

help probation officers feel and be more successful in their role as change agents.

Motivation has typically been captured in the Responsivity Principle of the RNR

framework (Bourgon & Bonta, 2014). However, one may argue that overlap exists between

motivation as a state factor (i.e., motivated or unmotivated) and criminogenic need items on the

YLS/CMI under the antisocial attitudes domain, specifically “does not seek help” and “actively

rejects help”. This overlap as both responsivity and criminogenic need can be puzzling from a

conceptual standpoint as there is a subtle distinction between clients who need motivation

addressed as a prelude or adjunct to increase their amenability to treatment (motivation as

responsivity) and those whose motivation issues are embedded within a more general antisocial

pattern that is causally linked to their offending behavior (motivation as a criminogenic need).

This becomes especially concerning when a youth’s antisocial attitude also acts as a barrier to

treatment for other criminogenic needs; the same might be true for a barrier like substance abuse.

If addressing barriers to treatment is a necessary prelude to providing treatment for criminogenic

needs, tackling the barriers that are criminogenic should be the first priority in treatment when


working with these youth, as the impact is twofold. Treating a youth’s antisocial attitudes

or substance abuse issues would address these criminogenic domain areas as well as open up a

‘window of opportunity’ for addressing other need areas (e.g., education, family, leisure) by

eliminating an important barrier to treatment.

Systems factors were also a category that had a broad impact on treatment match. Long

waitlists and a lack of programming, where the probation officer provided active case

management for their client’s needs and clients were sufficiently engaged and participated in

treatment, but programming simply was not available, interfered with treatment match at the

domain level (i.e., employment, leisure, and peers), as well as with the overall proportion of

criminogenic needs matched. A lack of programming in the leisure and peer domains (aside

from a gang exit program) likely made it difficult for probation officers to provide treatment

specified to these needs. In addition, programs that were easily accessible were often not

intensive enough, not evidence-based, or not administered by professionals with appropriate

training. Probation officers have commented on a lack of funding leading to a high turnover rate

in counselors, programs shutting down quickly, and long waitlists (Haqanee et al. 2015). The

broad effects of systems barriers require further research and attention from policymakers. The

need for a responsive system (Taxman, 2014) in order to meet the requirements of the

individuals within it, using a broad array of programming that varies along a continuum in terms

of intensity and targets, is starting to receive attention in the literature.

As expected, the presence of parental barriers resulted in lower odds of youth receiving

treatment match in the family domain, likely due to reduced parental participation. Parental

barriers were also associated with lower likelihood of treatment in the peer domain and a

reduction in the proportion of needs matched overall, which is in line with previous research that


states the importance of family involvement in juvenile justice processes (Henggeler &

Sheidow, 2012; Mendel, 2010).

Case management decisions facilitated treatment match in the employment domain,

suggesting that probation officers were more likely to prioritize employment needs over others.

This may have been for an engagement issue, a financial issue, or some combination of the two,

as it was observed anecdotally that many of the youth and their families were living in

government-subsidized housing. Programs like STICS (Strategic Training Initiative in

Community Supervision; Bonta et al., 2010) aim at helping probation officers prioritize

criminogenic needs. The STICS plan involves first dealing with acute needs or crises’ that may

require immediate attention (e.g., a homeless youth in the middle of winter), followed by

addressing criminogenic needs through the use of four central questions with themes arranged in

order of their causal relationship to recidivism: procriminal attitudes, interpersonal relationships,

problem behaviours, and other minor criminogenic needs (Bourgon et al., 2011, p. 12). Officers

in this sample seemed not to make case management decisions based on needs and their

relationship with recidivism, but based on needs that would be ‘high impact’. Probation officers

under-prioritized the leisure domain when making case management decisions, possibly because

there were a limited number of programs available to address this need or perhaps because this

need was one that could be targeted indirectly. When probation officers focused on finding

clients employment or ensured that clients were attending school, this indirectly ensured that

clients were making better use of their leisure time, therefore, the focus may have been placed on

addressing high impact needs like employment, instead of directly addressing the leisure domain.

Capacity issues and lifestyle and clinical destabilizers did not predict treatment match at

the domain or global level, possibly due to shared variance with other barriers.


Barriers to Treatment and Recidivism

To understand the relationship between these barriers and reoffending, criminal history

and overall (proportion) treatment match were looked at first. In line with previous research,

static risk predicted recidivism in the final model (Lowenkamp, Latessa & Holsinger, 2006),

however, lifestyle destabilizers and capacity issues (barrier categories that had no impact on

treatment match) better predicted recidivism in the final model than overall match. Perhaps in

this sample, rather than the overall ‘dosage’ of service, the critical issue was which needs were

serviced (i.e., need domains for which youth received high scores on the YLS/CMI may have

better predicted the likelihood of recidivism); this is an area for future research. This finding is

also interesting because according to the RNR model, lifestyle destabilizers and capacity issues

are only weakly related to recidivism and should be ignored or prioritized last in case

management. Research on “Second Generation of RNR” (Taxman, 2014), calls for expanding

the core principles of responsivity to include assessing destabilizing conditions and placing

offenders in programs based on their needs and destabilizers. Taxman and Caudy (2015) found it

was a complex array of criminogenic needs and destabilizers that predicted recidivism at a two-

year follow-up and not the risk classifications that are so often relied upon to predict future

criminal behavior. The results from the present study suggest that providing youth with an

outreach worker to help them with life functioning matters such as housing and financial issues

may support them to address problems that increase the odds of recidivism. In addition, these

results support the importance of adhering to the Responsivity Principle and delivering treatment

that fits with the learning style of individual youth.


Limitations and Future Directions

Results of this study present several directions for future research. This study examined

barriers to service in a sample of youth who received court-ordered assessments. This group

represents a small minority of justice-involved youth and they tend to present with higher rates

of serious charges than youth involved with the Canadian justice system as a whole (Thomas,

2008); having said that, participants’ average risk score fell in the moderate range, which

compares to scores of typical custodial youth (Hoge & Andrews, 2011). Furthermore, mental

health diagnoses did not differ from other studies in terms of rates and types of concerns (e.g.,

Shufelt & Cocozza, 2006; Skowyra & Cocozza, 2007; Stahlberg, Anckarsater, & Nilsson, 2010;

Teplin, Abram, McClelland, Dulcan, & Mericle, 2002; Wasserman, McReynolds, Lucas, Fisher,

& Santos, 2002; Wasserman, McReynolds, Schwalbe, Keating, & Jones, 2010) suggesting that

overall, although the youth in this sample came to be assessed through a relatively unique

avenue, they were quite similar to custodial youth receiving assessments more generally.

Probation case notes (i.e., documentation required for all contacts and collaterals) were

used to code for service-to-needs match and barriers to treatment. This source contains

important information about clients’ attendance, engagement, treatment, and participation with

school, family, work, peers, etc. However, the detail in this documentation varies across each

case and results are limited to the extent that the probation officer provides a complete account of

criminogenic need variables as well as barriers to treatment. While this retrospective review of

assessment information and case management is consistent with much of the research in this

area, studying the probation process prospectively, from assessment through case management,

would allow for observation of changes in youths’ risk scores, criminogenic needs, and barriers

over time, as well as the ability to make causal inferences about events associated with these


changes. Furthermore, additional research on the specific barriers that impede effective

case management and implementation of the RNR framework on the ground can better inform

probation officer training initiatives (EPICS: Smith et al., 2012; STARR: Robinson et al., 2012;

STICS: Bonta et al., 2011).

The Risk-Need-Responsivity approach to youth corrections utilizes a conceptual

framework continually informed by research evidence. A new wave of studies has focused on

examining how – and strengthening the success with which – the model is implemented in

community supervision practice. Of key concern in recent implementation research is the low

rate at which youths’ criminogenic needs are met during their probation terms. In the present

study we endeavoured to achieve a more detailed understanding of the factors that interfered

with treatment for criminogenic needs. Barriers to service included, but extended beyond, factors

currently captured under the concept of specific responsivity, and incorporated a broader class of

variables that Taxman and colleagues conceptualize as destabilizers and systemic responsivity

considerations. Barriers to treatment were significant in explaining low service-to-need match as

well as recidivism in this sample of youth. Results also indicated that risk factors, needs, and

barriers were related to one another. A fruitful direction for future research might thus be to

examine criminogenic needs and barriers in terms of clusters rather than individually in order to

suggest specific intervention approaches. This may provide information on how to better address

criminogenic needs while making responsivity considerations a bigger priority. Results also call

for greater attention to broader issues such as the need for a responsive system as well as

expanding the RNR framework to include room for the assessment and management of

destabilizing factors.


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Appendix A

Logistic Regression – Family Match Predicted by Criminal History and Seven Barrier Composites (n = 181)

CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper Model (Step) 1 YLS Criminal History -0.33 0.10 11.08 1 .001 0.72 0.60 0.87 Constant 0.14 0.22 0.40 1 .53 1.15 Overall model at Step 1 12.23 1 < .001 Model (Step) 2 YLS Criminal History -0.37 0.12 9.27 1 0.002 0.69 0.54 0.88 Lifestyle Destabilizers -0.15 0.25 0.37 1 0.54 0.86 0.53 1.39 Clinical Destabilizers 0.43 0.25 2.94 1 0.09 1.53 0.94 2.50 Capacity Issues 0.23 0.21 1.22 1 0.27 1.25 0.84 1.87 Engagement Issues -0.33 0.27 1.54 1 0.21 0.72 0.43 1.21 Parental Factors -1.50 0.36 17.20 1 <.001 0.22 0.11 0.45 Systems Factors -0.36 0.34 1.18 1 0.28 0.70 0.36 1.34 Case Management -0.18 0.26 0.44 1 0.51 0.84 0.50 1.41 Constant 1.15 0.42 7.63 1 0.01 3.16 Step 2 27.42 7 < .001 Overall model at Step 2 39.66 8 < .001


Appendix B

Logistic Regression – Education Match Predicted by Criminal History and Seven Barrier Composites (n = 201)

CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper

Model (Step) 1 YLS Criminal History -0.32 0.09 13.77 1 < .001 0.72 0.61 0.86 Constant 0.61 0.22 7.78 1 .01 1.84 Overall model at Step 1 14.80 1 < .001 Model (Step) 2 YLS Criminal History -0.17 0.10 2.71 1 .10 0.85 0.69 1.03 Lifestyle Destabilizers -0.31 0.21 2.16 1 .14 0.74 0.49 1.11 Clinical Destabilizers -0.09 0.23 0.17 1 .68 0.91 0.59 1.42 Capacity Issues -0.19 0.18 1.13 1 .29 0.83 0.58 1.18 Engagement Issues -0.79 0.24 10.59 1 .001 0.46 0.28 0.73 Parental Factors -0.23 0.22 1.06 1 .30 0.80 0.52 1.23 Systems Factors -0.11 0.29 0.13 1 .72 0.90 0.51 1.60 Case Management -0.45 0.24 3.59 1 .06 0.64 0.40 1.02 Constant 1.99 0.43 21.31 1 < .001 7.30 Step 2 31.22 7 < .001 Overall model at Step 2 46.03 8 < .001


Appendix C

Logistic Regression – Employment Match Predicted by Criminal History and Seven Barrier Composites (n = 104)


Model (Step) 1 YLS Criminal History -0.20 0.12 2.66 1 .10 0.82 0.65 1.04 Constant 0.23 0.31 0.53 1 .47 1.25 Overall model at Step 1 2.73 1 .10 Model (Step) 2 YLS Criminal History -0.04 0.16 0.07 1 .80 0.96 0.70 1.31 Lifestyle Destabilizers -0.43 0.32 1.84 1 .18 0.65 0.35 1.21 Clinical Destabilizers 0.06 0.39 0.02 1 .88 1.06 0.50 2.26 Capacity Issues -0.14 0.26 0.30 1 .59 0.87 0.52 1.45 Engagement Issues -0.37 0.39 0.87 1 .35 0.69 0.32 1.49 Parental Factors -0.74 0.41 3.29 1 .07 0.48 0.21 1.06 Systems Factors -0.86 0.43 3.94 1 .05 0.43 0.18 0.99 Case Management 1.18 0.39 9.12 1 .003 3.26 1.51 7.00 Constant 0.67 0.60 1.25 1 .26 1.96 Step 2 24.88 7 .001 Overall model at Step 2 27.61 8 .001


Appendix D

Logistic Regression – Substance Abuse Match Predicted by Criminal History and Seven Barrier Composites (n = 123)


Model (Step) 1 YLS Criminal History 0.11 0.13 0.65 1 .42 1.12 0.86 1.44 Constant -1.59 0.41 14.93 1 < .001 0.20 Overall model at Step 1 0.66 1 .42 Model (Step) 2 YLS Criminal History 0.21 0.15 2.01 1 .16 1.24 0.92 1.65 Lifestyle Destabilizers -0.29 0.30 0.91 1 .34 0.75 0.41 1.36 Clinical Destabilizers -0.06 0.42 0.02 1 .88 0.94 0.41 2.13 Capacity Issues 0.04 0.31 0.02 1 .90 1.04 0.57 1.90 Engagement Issues -0.39 0.34 1.27 1 .26 0.68 0.35 1.33 Parental Factors 0.03 0.32 0.01 1 .92 1.03 0.55 1.92 Systems Factors 0.09 0.46 0.04 1 .84 1.10 0.45 2.69 Case Management 0.18 0.36 0.25 1 .62 1.19 0.59 2.40 Constant -1.43 0.65 4.90 1 .03 0.24 Step 2 4.35 7 .74 Overall model at Step 2 5.01 8 .76


Appendix E

Logistic Regression – Personality Match Predicted by Criminal History and Seven Barrier Composites (n = 177)


Model (Step) 1 YLS Criminal History -0.26 0.94 7.60 1 .01 0.77 0.64 0.93 Constant 0.50 0.24 0.04 1 .84 1.05 Overall model at Step 1 8.05 1 .01 Model (Step) 2 YLS Criminal History -0.20 0.11 3.44 1 .06 0.82 0.67 1.01 Lifestyle Destabilizers -0.25 0.22 1.24 1 .27 0.78 0.51 1.21 Clinical Destabilizers 0.16 0.24 0.40 1 .53 1.17 0.72 1.88 Capacity Issues -0.23 0.19 1.56 1 .21 0.79 0.55 1.14 Engagement Issues -0.62 0.25 5.97 1 .02 0.54 0.33 0.89 Parental Factors -0.33 0.24 1.87 1 .17 0.72 0.44 1.16 Systems Factors -0.51 0.31 2.67 1 .10 0.60 0.33 1.11 Case Management 0.23 0.25 0.83 1 .36 1.25 0.77 2.04 Constant 0.98 0.42 5.33 1 .02 2.65 Step 2 17.92 7 .01 Overall model at Step 2 25.97 8 .001


Appendix F

Logistic Regression – Attitude Match Predicted by Criminal History and Seven Barrier Composites (n = 117)


Model (Step) 1 YLS Criminal History -0.11 0.13 0.69 1 .41 0.90 0.70 1.16 Constant -1.07 0.35 9.31 1 .002 0.34 Overall model at Step 1 0.70 1 .40 Model (Step) 2 YLS Criminal History -0.07 0.15 0.22 1 .64 0.93 0.69 1.26 Lifestyle Destabilizers -0.08 0.28 0.09 1 .77 0.92 0.53 1.61 Clinical Destabilizers -0.36 0.36 1.02 1 .31 0.70 0.34 1.41 Capacity Issues -0.48 0.37 1.66 1 .20 0.62 0.30 1.28 Engagement Issues -0.66 0.37 3.13 1 .08 0.52 0.25 1.07 Parental Factors -0.78 0.48 2.66 1 .10 0.46 0.18 1.17 Systems Factors 0.20 0.34 0.37 1 .55 1.23 0.64 2.36 Case Management 0.09 0.58 0.02 1 .88 1.09 Constant -0.07 0.15 0.22 1 .64 0.93 0.69 1.26 Step 2 12.11 6 .06 Overall model at Step 2 12.81 7 .08


Appendix G

Logistic Regression – Leisure Match Predicted by Criminal History and Seven Barrier Composites (n = 163)


Model (Step) 1 YLS Criminal History -0.29 0.11 6.41 1 .01 0.75 0.60 0.94 Constant -0.49 0.26 3.65 1 .06 0.61 Overall model at Step 1 7.05 1 .01 Model (Step) 2 YLS Criminal History -0.06 0.15 0.17 1 .68 0.94 0.70 1.26 Lifestyle Destabilizers -0.21 0.30 0.50 1 .48 0.81 0.46 1.45 Clinical Destabilizers -0.27 0.36 0.58 1 .45 0.76 0.38 1.53 Capacity Issues -0.07 0.24 0.09 1 .77 0.93 0.58 1.50 Engagement Issues -1.62 0.44 13.58 1 < .001 0.20 0.08 0.47 Parental Factors -0.14 0.32 0.18 1 .67 0.87 0.47 1.62 Systems Factors -0.95 0.43 4.93 1 .03 0.39 0.17 0.89 Case Management -0.68 0.33 4.20 1 .04 0.51 0.27 0.97 Constant 1.27 0.50 6.51 1 .01 3.54 Step 2 43.60 7 < .001 Overall model at Step 2 50.65 8 < .001


Appendix H

Logistic Regression – Peer Match Predicted by Criminal History and Seven Barrier Composites (n = 173)


Model (Step) 1 YLS Criminal History -0.34 0.13 6.94 1 .01 0.71 0.56 0.92 Constant -0.83 0.27 9.45 1 .002 0.44 Overall model at Step 1 7.97 1 .01 Model (Step) 2 YLS Criminal History -0.22 0.15 2.10 1 .15 0.80 0.59 1.08 Lifestyle Destabilizers -0.61 0.32 3.51 1 .06 0.55 0.29 1.03 Clinical Destabilizers -0.15 0.34 0.19 1 .66 0.86 0.44 1.68 Capacity Issues -0.27 0.27 1.06 1 .30 0.76 0.45 1.28 Engagement Issues -0.01 0.35 <0.001 1 .99 0.99 0.50 1.96 Parental Factors -0.88 0.39 5.08 1 .02 0.42 0.19 0.89 Systems Factors -1.34 0.49 7.47 1 .01 0.26 0.10 0.68 Case Management -0.49 0.32 2.35 1 .13 0.61 0.33 1.15 Constant 1.09 0.52 4.40 1 0.04 2.96 Step 2 28.07 7 < .001 Overall model at Step 2 36.04 8 < .001

barriers to effective rnr implementation for youth in … · anjani kapoor master of arts applied...

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