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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
Barrier Composites (n = 177) …………………………………………………………………...54
Appendix F: Logistic Regression – Attitude Match Predicted by Criminal History and Seven
Barrier Composites (n = 117) …………………………………………………………………...55
Appendix G: Logistic Regression – Leisure Match Predicted by Criminal History and Seven
Barrier Composites (n = 163) …………………………………………………………………...56
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
BARRIERS TO RNR IMPLEMENTATION 2
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).
BARRIERS TO RNR IMPLEMENTATION 3
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
BARRIERS TO RNR IMPLEMENTATION 4
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
BARRIERS TO RNR IMPLEMENTATION 5
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;
BARRIERS TO RNR IMPLEMENTATION 6
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).
BARRIERS TO RNR IMPLEMENTATION 7
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
BARRIERS TO RNR IMPLEMENTATION 8
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
BARRIERS TO RNR IMPLEMENTATION 9
(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
BARRIERS TO RNR IMPLEMENTATION 10
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.,
BARRIERS TO RNR IMPLEMENTATION 11
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.
BARRIERS TO RNR IMPLEMENTATION 12
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.
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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
BARRIERS TO RNR IMPLEMENTATION 14
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
BARRIERS TO RNR IMPLEMENTATION 15
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)
BARRIERS TO RNR IMPLEMENTATION 16
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
BARRIERS TO RNR IMPLEMENTATION 17
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
BARRIERS TO RNR IMPLEMENTATION 18
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.
BARRIERS TO RNR IMPLEMENTATION 19
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
BARRIERS TO RNR IMPLEMENTATION 20
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
BARRIERS TO RNR IMPLEMENTATION 21
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.
BARRIERS TO RNR IMPLEMENTATION 22
= 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
BARRIERS TO RNR IMPLEMENTATION 23
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
BARRIERS TO RNR IMPLEMENTATION 24
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
BARRIERS TO RNR IMPLEMENTATION 25
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.
BARRIERS TO RNR IMPLEMENTATION 26
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.
BARRIERS TO RNR IMPLEMENTATION 27
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
Overall Match β = -.15
Parental Factors
Family OR = .35
Peer OR = .42
Overall Match β = -.23
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
BARRIERS TO RNR IMPLEMENTATION 28
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
BARRIERS TO RNR IMPLEMENTATION 29
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
BARRIERS TO RNR IMPLEMENTATION 30
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
BARRIERS TO RNR IMPLEMENTATION 31
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,
BARRIERS TO RNR IMPLEMENTATION 32
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.
BARRIERS TO RNR IMPLEMENTATION 33
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
BARRIERS TO RNR IMPLEMENTATION 34
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
BARRIERS TO RNR IMPLEMENTATION 35
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 RNR IMPLEMENTATION 36
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.
BARRIERS TO RNR IMPLEMENTATION 37
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
BARRIERS TO RNR IMPLEMENTATION 38
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.
BARRIERS TO RNR IMPLEMENTATION 39
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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
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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
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Appendix C
Logistic Regression – Employment Match Predicted by Criminal History and Seven Barrier Composites (n = 104)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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Appendix D
Logistic Regression – Substance Abuse Match Predicted by Criminal History and Seven Barrier Composites (n = 123)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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Appendix E
Logistic Regression – Personality Match Predicted by Criminal History and Seven Barrier Composites (n = 177)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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Appendix F
Logistic Regression – Attitude Match Predicted by Criminal History and Seven Barrier Composites (n = 117)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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Appendix G
Logistic Regression – Leisure Match Predicted by Criminal History and Seven Barrier Composites (n = 163)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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Appendix H
Logistic Regression – Peer Match Predicted by Criminal History and Seven Barrier Composites (n = 173)
CI (95%) Model Variables β SE β Wald’s χ2 df p Exp(B) Lower Upper
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
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