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Criminal Justice Policy Review
24(1) 94 –122
© 2013 SAGE Publications
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DOI: 10.1177/0887403412447505
http://cjp.sagepub.com
447505CJP24110.1177/0887403412447505Mc
Kiernan et al.Criminal Justice Policy Review
1University of Louisville, Louisville, KY, USA
2Pacific Institute for Research and Evaluation, Louisville Center, KY, USA
3COPES, Inc., Louisville, KY, USA
Corresponding Author:
Patrick McKiernan, University of Louisville, 321 W. Main Street, Suite 390, Louisville, KY 40202, USA
Email: Patrick.McKiernan@ky.gov
Creating Lasting Family
Connections: Reducing
Recidivism With
Community-Based Family
Strengthening Model
Patrick McKiernan1, Stephen R. Shamblen2,
David A. Collins2, Ted N. Strader3,
and Christopher Kokoski3
Abstract
There is increasing evidence of the effectiveness of continued care after reentry for those
who have participated in prison-based substance abuse treatment. This article presents
results from analyses of program and comparison group data from two community-
based programs that implemented a culturally adapted version of the Creating Lasting
Family Connections (CLFC) curriculum. Both programs sought to strengthen individuals
(and their families) recently reentering the community after incarceration. Results
suggested that the first program had effects on increasing HIV knowledge and spirituality,
while reducing intentions to binge drink and recidivism. The second program similarly
showed effects on recidivism, and participants also showed an increase in nine separate
relationship skills. The policy implications of the results are discussed.
Keywords
recidivism, reentry, substance abuse, family strengthening, relationship skills
Over three decades, the United States has expended excessive amounts of resources
and time building prisons as a primary means for handling drug offenders. As the costs
for this approach have increased without the expected decrease in criminal offenses,
McKiernan et al. 95
new strategies in addressing the problem of drugs and crime have become necessary.
Starting in 2005, the national trend to improve outcomes and reduce costs, initiated
through the Second Chance Act, opened the door for nontraditional methods for work-
ing with reentry populations (Pogorzelski, Wolff, Pan, & Blitz, 2005). Throughout the
nation, and specifically in Kentucky, this trend represented a shift in focus from pun-
ishment to rehabilitation in an effort to reduce both costs and recidivism. This article
describes and discusses one such nontraditional method, and the authors propose poli-
cies predicated on the findings of the reported studies.
Background
Most drug-involved offenders return to society from prison without having received
any substance abuse treatment (Taxman, Perdoni, & Harrison, 2007). Many in this
group commit technical violations of parole within 3 years (24% nationally and 20%
in Kentucky), which represents half of those returning to prison in Kentucky (PEW,
2011). Efforts to reduce the cycle of recidivism with substance abusing offenders
place greater attention on connecting reentry prisoners with addiction treatment pro-
viders in the community (Sung, Belenko, & Feng, 2001). Outcome studies examining
drug use and recidivism of participants in corrections-based substance abuse treat-
ment programs have documented successful reductions in both areas over the last two
decades (e.g., Burdon, Dang, Prendergast, Messina, & Farabee, 2007; Inciardi,
Martin, Butzin, Hooper, & Harrison, 1997; Knight, Simpson, Chatham, & Camacho,
1997; Prendergast, Hall, Wexler, Melnick, & Cao, 2004). Moreover, offenders who
participate in prison-based treatment and continued care after community reentry
experience reduced relapse and recidivism above and beyond that of prison-based
treatment alone (Burdon et al., 2007; Hiller, Knight, & Simpson, 1999; Inciardi et al.,
1997; Martin, Butzin, Saum, & Inciardi, 1999).
Studies of cognitive-behavioral treatments (including treatment provided specifi-
cally to drug users) were shown to be effective among offender populations (Lipsey,
Chapmen, & Landenberger, 2001). Related studies of cognitive behavioral approaches
delivered in prison-based treatment programs that follow inmates to community
release have demonstrated success, defined as abstinence from substance use and a
reduction in recidivism (Pelissier, Motivans, & Rounds-Bryant, 2005). A review of
evidence-based approaches has found an association between treatment modality and
other treatment components (e.g., having staff training specialists, the provision of
ancillary services) and outcomes (Schildhaus, Gerstein, Dugoni, Brittingham, &
Cerbone, 2000). Other studies of cognitive behavioral approaches with criminal
offenders demonstrate similar positive results (Roberts-Lewis, Parker, Welch, Wall, &
Wiggins, 2009). Dowden and Andrews (1999) conducted a systematic review on
effective correctional treatment for adult offenders through the use of meta-analysis.
On the basis of their statistical review, they found cognitive-behavioral interventions
and social learning methods were more effective correctional interventions than those
based on nonbehavioral approaches. Cognitive-behavioral approaches have also been
96 Criminal Justice Policy Review 24(1)
found to be effective with populations of adolescents with a substance use disorder
(Dennis et al., 2004; Waldron, Slesnick, Brody, Turner, & Peterson, 2001), and those
with co-occurring substance use and other mental health disorders (Kaminer, Burleson,
& Goldberger, 2002).
Because of prison overcrowding and the expense of incarceration, many states have
aggressively developed early release initiatives and established policies to reduce
recidivism (Anglin, Brown, Dembo, & Leukefeld, 2009). Research on treatment of
substance abusing criminal offenders and outcomes supports the need for effective
treatment approaches. This research further identified the importance of policy revi-
sion related to the successful diversion from prison and effective postrelease strategies
for inmates exiting prison to ensure continued treatment at reentry (Jolley & Kerbs,
2010). Taxman (2009) provides further support in testimony before the Congressional
Subcommittee on Commerce, Justice, Science, and Related Agencies, where it was
concluded, “the community component is critical to sustained results”(p. 3).
To this end, many states initiated a policy shift from a punishment focus to balanc-
ing punishment and treatment (Taxman, 2008). The Second Chance Act of 2005
acknowledged that the 600,000 inmates exiting prison each year need access to
resources and opportunities that allow and encourage positive participation in society
to reduce recidivism and increase public safety (Pogorzelski et al., 2005). Kentucky
Governor Steve Beshear (March 4, 2011) signed into law revisions to the penal code
with the goal of reducing recidivism to help lower the cost of incarceration through the
combination of diversion programs, substance abuse treatment, and early release pro-
grams that enhance community supervision and collaboration with community service
providers. This legislation identified the need to address excessive recidivism that
peaked at 44% in 2003 and stood at 40% in 2007 (Pew Center, 2011). According to the
Pew Center report (2011), recidivism in Kentucky is described as prisoners returning
within 3 years.
Description of the Intervention
In 2000, The Kentucky Department of Corrections (KDOC) began addressing the
recidivism problem by increasing the availability of substance treatment programs:
six prison programs (increased from four programs) and 18 regional jail programs
(increased from two programs; Staton-Tindall et al., 2009). During the same period,
the Kentucky Department of Corrections increased collaboration with community-
based treatment and prevention organizations to expand support of reentry popula-
tions with the goals of reducing recidivism and increasing community protection. In
particular, the KDOC sought to find partners that offered community-based program-
ming designed to advance aftercare services using evidence-based approaches identi-
fied as effective in addressing deficits in multiple domains (e.g., psychiatric,
employment, and family problems; Huebner & Cobbina, 2007). This search resulted
in the KDOC developing a partnership with the Council on Prevention and Education:
Substances, Inc. (COPES, Inc.).
McKiernan et al. 97
Between 2005 and 2011, COPES, Inc. implemented two separate collaborative,
community-based efforts to strengthen individuals (and their families) recently reen-
tering the community after incarceration. The projects were designed specifically to
(a) reduce substance abuse, prison recidivism, and HIV/Hepatitis infection rates and
(b) promote fatherhood and relationship skills and healthy sexual practices among
adult males reentering the Louisville, Kentucky Metro community. The two projects
especially targeted services for those individuals who had received substance abuse
treatment while incarcerated.
Both projects implemented the same culturally adapted version of the evidence-
based model program, Creating Lasting Family Connections (CLFC), which included
a thoroughly integrated HIV/Hepatitis preventive intervention component. CLFC is
listed on Substance Abuse and Mental Health Services Administration’s (SAMHSA;
2011) National Registry of Evidence-based Programs and Practices (NREPP).
The CLFC program was further adapted by Ted N. Strader (COPES, Inc. Executive
Director and CLFC program developer) for cultural sensitivity for this specific reentry
target population in 2004. The adapted CLFC program is designed to increase skills
that individuals and families find useful in reestablishing strong family harmony and
support for recovery and reentry, and to assist parents in gaining deep insight in pro-
viding effective prevention for their children. The adapted intervention addressed mul-
tiple challenging and interconnected issues (family strengthening, substance abuse
[and recovery support], violence, HIV/Hepatitis, and prison recidivism) with a multi-
faceted approach. This approach was specifically designed to cultivate an atmosphere
of inclusion, respect, and cultural sensitivity to an at-risk audience traditionally con-
sidered to be somewhat resistant and difficult to recruit and retain in a program of
significant scope and duration.
This adaptation of CLFC included the three (multisession) adult facilitator-led,
group-learning CLFC modules, plus a brief new module on HIV and other sexually
transmitted disease prevention and sexual health. Collectively, these four components
involved 20 sessions delivered in 2-hr classes provided once or twice per week.
Because program sessions were often held immediately after working hours (from
6:00 to 8:00 p.m.), light meals and informal contact with the program staff were avail-
able for any participant who was interested one-half hour before each session. The
meals and contact with staff served as both a convenience for participants and an
incentive for participation. Typically, two state Certified Substance Abuse Prevention
Specialists and/or Certified Alcohol and Drug Counselors (CADC) cofacilitated the
program sessions. The intervention was offered at multiple times (i.e., morning, after-
noon, and evening) at the COPES, Inc. office and at various local sites throughout the
Metro Louisville, Kentucky area accessible to the target population.
The four highly interactive CLFC program modules represent a growing body of
research regarding the role of family in recovery and changing family systems to sup-
port recovery. Studies on family and concerned others of substance abusing persons
have consistently demonstrated in replicated randomly controlled trials that involve-
ment of family in interventions on resistant addicts (Landau et al., 2000, 2004; Meyers,
98 Criminal Justice Policy Review 24(1)
Miller, Smith, & Tonigan, 2000; Meyers, Smith, & Lash, 2005) and in treatment that
teach behavioral skills to reduce enabling and support the addict in recovery (McCrady,
1989; McCrady, Epstein, & Hirsch, 1996; Meyers et al, 2000; Miller, Meyers, &
Tonigan, 1999; Rotunda & O’Farrell, 1998; Stanton, 2004; Stanton & Heath, 2005;
Stanton & Shadish, 1997; Staton-Tindall, McNees, Walker, & Leukefeld, 2007;
Velleman, 2006; Yoshioka, Thomas, & Ager, 1992) significantly reduce substance
abuse across the following year.
Furthermore, best practices call for the use of a combination of family systems and
functional analysis for assessment provided with cognitive and behavioral methods to
initiate change in family members and/or the substance abuser (e.g., Kelley & Fals-
Stewart, 2002; McCrady, Epstein, & Hirsch, 1999; Nelson & Sullivan, 2007; O’Farrell
et al., 1996a, 1996b; O’Farrell & Fals-Stewart, 2000; Powers, Vedel, & Emmelkamp,
2008). Studies of substance abuse treatment identify positive outcomes following
CBT interventions, coping skills training, identification and elimination of cognitive
distortions, and development of refusal skills (Marlatt & Donovan, 2005; Monti,
Abrams, Kadden, & Cooney, 1989). Other studies note the role of relapse prevention
and development of self-control skills (Marlatt, Parks, & Witkiewitz, 2002). In addi-
tion, assessment scales, functional analyses, and feedback are ideally suited as group
methods, and cognitive and behavioral skills training are currently delivered as pri-
mary interventions across a variety of inpatient and outpatient settings (Dennis, Foss,
& Scott, 2007).
Finally, the CLFC program is based on Risk and Resiliency Theory with an empha-
sis on strengthening resiliency factors for individuals, their families, and their com-
munities (Strader, Collins, & Noe, 2000). Much research has been devoted to factors
that may account for successful outcomes for individuals who face high risks
(Garmezy, 1985; Hawkins, Catalano, & Miller, 1992). Risk factors can include early
and persistent problems such as substance use, delinquent/criminal behavior, associa-
tion with peers that model problem behavior, and poor family relationships. Braverman
(1999, 2001) has noted that there is a great deal of overlap between research on resil-
iency and research on substance abuse prevention. The resilience literature tends to
take a broader view, focusing not just on substance abuse, but on the larger issues of
adjustment and adaptation. Resnick (2000) has also noted that the resiliency paradigm,
which emphasizes strengths, resources, and assets as opposed to the “restatement of
pathology” that has characterized much of the research on communities of color, reso-
nates with and often finds acceptance among minority constituents.
The CLFC modules, “Developing Positive Parental Influences,” “Raising Resilient
Youth,” “Getting Real,” and “The ABC 3D Approach to HIV Prevention,” represent a
delivery method that includes elements of each of the aforementioned approaches. A
brief description of each module is outlined below:
• “Developing Positive Parental Influences” is a training that promotes a deep
awareness of personal thoughts, feelings, attitudes, beliefs, and experiences
along the continuum of chemical use, abuse, and dependency. This module
McKiernan et al. 99
also examines effective approaches for intergenerational family prevention,
along with providing a practical understanding of intervention, referral pro-
cedures, and treatment options including ongoing aftercare. In addition, the
Developing Positive Parental Influences training includes an in-depth look
at the dynamics of chemical dependency and its impact on families, and the
promise of abstinence and recovery for the entire family.
• “Raising Resilient Youth” is a training on a broad range of relationship skills
for individual and family strengthening. In this component, individuals (and
their families) are asked to learn and practice effective communication skills,
including listening and validating others’ thoughts and feelings, and to learn
and practice how to successfully manage their own thoughts and feelings.
Individuals and families are also asked to examine and enhance their ability
to develop and implement expectations and consequences with others includ-
ing spouses, coworkers, friends, and children in all areas of interest and con-
cern. Parents are taught how to include their children’s active participation
in setting both expectations and consequences on a wide variety of important
issues of interest or concern to the parent, including alcohol and drugs. This
encourages dialogue, which enhances a sense of competence, connectedness,
and bonding between parent and child.
• “Getting Real” is a training that invites participants to examine their responses
to the verbal and nonverbal behavior they experience in their interactions
with others, and offers personalized coaching on effective communication
skills, including speaking with confidence and sensitivity, listening to and
validating others, sharing feelings, and matching body language with verbal
messages. The Getting Real module promotes the skills of self-awareness
and mutual respect, while focusing on helping participants combine thoughts,
feelings, and behavior in a way that leads them to generate powerful and
meaningful messages to others.
• “The ABC 3D Approach to HIV Prevention” is a serious, yet often humor-
ous and candid, examination of the primary modes of transmission of HIV,
hepatitis, and other sexually transmitted diseases. This training concludes
with effective preventive measures to reduce or eliminate risk of infection.
Healthy sexual expression is recognized, discussed, and supported. During
this component, participants are also offered voluntary, free, rapid, confiden-
tial, and on-site HIV testing.
Operating under the theory that effective reentry programs both reduce risk factors
and promote resilience factors, the CLFC program focused on enhancing the condi-
tions and experiences (resiliency or protective factors) that appear to protect individu-
als from initiating or reengaging in alcohol, tobacco, and other drug use. The
operational factors may vary drastically for individuals across the spectrum of socio-
economic status. Our past research and experience has shown that resilient individuals
100 Criminal Justice Policy Review 24(1)
can avoid drug use, abuse and prison recidivism even when multiple and severe risk
factors are present. Because these two projects served minority adult ex-offenders
who had received substance abuse treatment while incarcerated, the program focused
on relapse and prison recidivism prevention and broadly enhancing other strengths
and positive resiliency factors.
A key factor in our theoretical approach to effective treatment and prevention is
human “connectedness.” Research on adolescents identifies family connectedness as
one of the most important factors for psychological well-being and positive outcomes
(Blum & Reinhardt, 1997; Doll & Lyon, 1998; Field, Diego, & Sanders, 2001).
Similarly, social support systems represent an important variable in treatment compli-
ance and outcomes for men (Booth et al., 1992). Other studies (Knight & Simpson,
1996) found that improved personal relationships during treatment improved out-
comes, such as reduced drug use and greater program compliance.
Connectedness means feeling emotionally close, cared about, and listened to in
one’s family, with significant others outside of our family, and with others in the
broader community. Furthermore, when “connected,” one is able to express personal
thoughts and feelings, and to discover that one’s self and one’s family are rooted in—
and connected to—a community of “others” in significant and meaningful ways.
Feeling or perceiving one’s self to be connected (to self, family, and community)
appears to create a protective shield of resiliency and strength to resist problem behav-
iors. The CLFC model proposes that connectedness is a critical protective and healing
force in human beings—young or old, rich or poor, male or female. Deep, healthy
human connections build strong protective shields (or immunity) to prevent harm and
provide both nurturing and healing support, even when challenges penetrate this
shield. From this reference came the title, “The Connect-Immunity Project.” For a
complete review of the underlying beliefs embedded in the CLFC intervention, please
see Building Healthy Individuals, Families, and Communities: Creating Lasting
Connections (Strader et al., 2000, p. 124).
Another key component of the CLFC intervention included comprehensive, com-
passionate, and culturally sensitive case management services to participants. Case
managers provided caring support, advice, and referral to other services in the com-
munity to address a wide range of barriers to recovery and reentry, and to promote
retention (i.e., job search skills, child care issues, transportation, etc). Case manage-
ment services were offered to the individual and their family during the initial assess-
ment, prior to and during the program, and for up to a year after enrollment into the
program.
Prior to implementing the CLFC intervention, COPES, Inc. conducted a compre-
hensive, year-long community needs assessment to discover gaps in services, built
organizational and community capacity by developing a coalition of community agen-
cies to equip the community to fill service gaps discovered during the needs assess-
ment process, initiated strategic planning for the program based on findings of the
needs assessment process with our program partner agencies, and included input from
McKiernan et al. 101
focus group members of the target population. COPES also selected, employed, and
trained staff in both the preventive intervention and in cultural competency for local
reentry populations.
Therefore, under the leadership and supervision of COPES staff, the Kentucky
Department of Corrections (KDOC), Dismas Charities, the KDOC Social Service
Clinicians and Probation and Parole representatives created the Joint Intervention
Meeting (JIM) where key partner agency staff representatives met both privately and
jointly with selected reentry clients to provide a collective and consistent message of
strong support, cultural sensitivity, respect, understanding, and accountability. Reentry
participants responded very favorably to an increasingly respectful, positive, affirm-
ing, culturally sensitive and uplifting strengths-based approach. Rather than waiting
until negative behaviors escalated into criminal violations where severe consequences
were required, these JIM meetings were designed to address early warning signs of
behavioral slippage and redirect participants onto a positive path of reentry and recov-
ery in a proactive and supportive manner prior to the need for major sanctions. During
planned JIM meetings, a collective group of interagency staff representatives met with
clients to review client complaints and client compliance or manageability concerns
(including absences, tardiness, positive drug screens, low motivation, unemployment,
housing issues, etc). The meetings were designed to provide a wide safety net of both
support and personal accountability for each participant’s successful reentry and
recovery, while encouraging healthy decision making and long-term personal and
family stability.
In addition, COPES established a quasi-experimental evaluation design with par-
ticipants and comparison groups to evaluate the efficacy of the program by administer-
ing baseline, exit, and follow-up surveys, and by administrating retrospective surveys
following each facilitator-led, group-learning module. COPES also administered an
annual Collaborating Partner Survey, and collected HIV testing and prison recidivism
data using verifiable recorded data from the Kentucky Department of Corrections.
Program participants were given the opportunity to provide feedback at the end of
each of the three primary group-learning modules and at the end of each complete
program run for real-time quality control. Finally, COPES incorporated comprehen-
sive, long-term planning for sustainability with project partner agencies. The follow-
ing two studies use a quasi-experimental methodology to examine (Study 1) the effects
of CLFC on antisocial behavior and recidivism and (Study 2) the effects of CLFC on
relationship skills and recidivism. These studies are reported in turn.
Study 1
Method
Participants. The participants for the present study were 249 individuals who partici-
pated in the intervention group and 96 individuals who participated in the comparison
group. The participants were predominately male in both the intervention (76%) and
102 Criminal Justice Policy Review 24(1)
comparison (78%) group and in their mid-30s (intervention: 34.68 and comparison:
37.13). About half of the participants were African American (53% in both groups)
and very small proportions were Hispanic (intervention: .44% and comparison: 2%).
The participants were predominately of low socioeconomic status, as about one quar-
ter were independently housed (intervention: 23% and comparison: 27%), about one
half were employed (intervention: 51% and comparison: 53%), and the majority of
participants had an income at or under US$30,000 (intervention: 80% and compari-
son: 82%). The majority of participants had either a high school diploma or a GED
(intervention: 82% and comparison: 81%). The majority of participants reported that
they had a heterosexual sexual orientation (95% in both groups).
Selectivity biases. Two alternative explanations for putative study findings are that
(a) intervention effects could be due to nonrandom assignment of individuals to the
intervention and comparison groups (i.e., a quasi-experimental design) and (b) inter-
vention effects could be due to participants who are likely to exhibit negative out-
comes being more likely to drop out of the study, especially in the intervention group.
Both of these potential sources of selectivity biases were addressed using a Heckman
two-step procedure (Heckman, 1976, 1979). This approach involves regressing either
(a) intervention group or (b) attrition status on participant background characteristics
in the first step using a probit regression model. The second step involves producing
predicted scores, where these scores are transformed to an inverse Mill’s ratio (IMR),
and the IMR is included in all inferential analyses. These methods are not subject to
the same biases that characterize propensity methods.
Prior to performing the first step probit models, missing background characteristic
data were imputed using the Expectation Maximization (EM) algorithm in SPSS 18.0.
EM employs maximum-likelihood estimation to ensure consistency between the vari-
ance-covariance matrix derived from the observed data and the imputed data
(Dempster, Laird, & Rubin, 1977). All background characteristics mentioned in the
participants section were used as predictors and outcomes in the EM model. As the amount
of missing data were minimal (less than 5% for any variable) and due to the necessity
of eliminating any case with any missing background characteristic, we felt that impu-
tation posed fewer inferential risks than eliminating entire cases.
Our first probit model examined selectivity biases due to assignment to the inter-
vention or comparison group. There was no evidence to suggest that any of the back-
ground characteristics predicted intervention group assignment (ps > .05) and the
overall model did not predict intervention group assignment, χ2(335) = 345.29, p = .34.
As there was no evidence of bias due to assignment to the intervention or comparison
group, we did not create an IMR representing this source of selectivity bias.
Our second probit model examined selectivity biases due to attrition. Of the 345
participants, 70% completed all three waves of the study, 2% completed waves one
and two of the study, and 28% completed only wave one of the study. Our model sug-
gested that individuals without a high school education or GED were more likely to
drop out of the study, z = –2.26, p = .02; however, the overall model did not predict
attrition, χ2(335) = 341.20, p = .40. As we did have one significant predictor of
McKiernan et al. 103
attrition, we did produce an IMR representing selectivity bias due to attrition, which
was included as a covariate in all of our inferential models.
Support persons. There were 23 individuals in the intervention condition who par-
ticipated at all three waves; however, they were support persons of the participants
who were previously incarcerated. We performed all inferential analyses reported later
with and without these individuals. The pattern of results and all statistical signifi-
cance decisions were the same when we included or excluded these individuals. Due
to these 23 persons not necessarily being the intended targets of treatment and due to
their not being a comparable subsample of individuals in the comparison group, the
remainder of this report only reports inferential analyses excluding these 23 individuals.
Procedure. Initial relationships were developed with the Kentucky Department of
Corrections prior to implementing a system of acquiring participants for the interven-
tion and control conditions. Participants were assigned the intervention and compari-
son conditions using a semirandom process. Due to assignment not being completely
random assignment (i.e., every participant did not have an equal probability of being
assigned to the intervention or comparison condition), this study must be considered
as a quasi-experimental study.
As there is a constant stream of individuals being released from the prison system,
individuals released within a span of several months were clustered together into
cohorts for a total of 15 cohorts. These participants were released from prison between
the years of 2006 and 2010. The only thing defining cohort is when individuals were
released from prison, which is a function of sentencing. Thus, there is nothing to sug-
gest that there should be variability among cohorts, such as a violation of the stable use
treatment value assumptions (SUTVA; Rubin, 1974). For large cohorts, every other
person was assigned to the intervention group.
The survey was administered to all participants at baseline, exit, and follow-up.
Surveys were administered by program staff. Informed consent was first required from
all participants before completing the survey. All participants were informed that their
participation in the survey was voluntary and their decision to not complete the survey
would not affect their participation in the program. Additionally, participants were
informed that their responses were anonymous and would not be shared, except in
aggregate form for reporting purposes. Full proctoring (i.e., staff reading the survey to
participants) was offered to those participants who had difficulty reading. Completed
surveys were placed in a sealed envelope and sent to the evaluator for data entry and
analysis.
Measures. Participants completed a questionnaire at each of the three waves of the
study, which inquired about various antisocial outcomes (e.g., attitudes, behaviors,
cognitions) and criminal outcomes (i.e., recidivism). We examined whether all items
purported to measure an underlying construct were measuring the same underlying
construct by calculating Cronbach’s alpha at Time 1. Furthermore, scores for multi-
item scales were calculated by taking the average of responses to items comprising the
scale, unless otherwise noted. The specifics of these measures appear in Table 1.
Among other measures not reported here, the questionnaire included measures of the
following constructs.
104 Criminal Justice Policy Review 24(1)
Substance use was measured with six items inquiring about substance use in the
past 30 days, where participants indicated the number of days they had used the sub-
stance or engaged in the behavior in the past 30 days. Specifically, participants were
asked about cigarette use, other tobacco use (e.g., smokeless tobacco), alcohol use,
drunkenness, marijuana use, and other illegal drug use.
Perceived great risk of substance use (α = .72) was measured with three items
assessing the degree to with which participants thought people would risk harming
themselves if they engaged in a moderate level of cigarette, alcohol, and marijuana
use. Participants responded to items using a 1 (no risk) to 4 (great risk) scale.
Number of types of risky sexual behaviors was assessed with five items inquiring
about whether a risky sexual behavior (e.g., Have you ever had unprotected sex with
someone whom you knew was, or suspected of being, an injected drug user?) had
occurred in the past 3 months. A count of yes responses was taken for these items.
Number of types of unprotected sex acts was measured with three items where partici-
pants indicated if they had unprotected oral, vaginal, or anal sex the last time they
engaged in sexual activities. A count of yes responses was taken for these items. Using
barrier methods becomes less important with a single and consistent sex partner, espe-
cially if the goal is to reduce the likelihood of sexually transmitted diseases. As such,
Table 1. Psychometrics for Outcome Measures
No. of Items Range Alpha Time 1
No. of days cigarettes used (in past 30) 1 0-30 n/a
No. of days other tobacco used (in past 30) 1 0-30 n/a
No. of days alcohol used (in past 30) 1 0-30 n/a
No. of days drunk (in past 30) 1 0-30 n/a
No. of days marijuana used (in past 30) 1 0-30 n/a
No. of days other illegal drugs used (in past 30) 1 0-30 n/a
Perceived great risk of substance use 3 1-4 .72
No. of types of unprotected sex acts (last time)a3 0-3 .69
No. of types of risky sexual behaviors (past 3 months)a5 0-5 .34
Perceived risk of risky sexual behavior 6 1-4 .82
Knowledge 18 0-100 .78
Future high likelihood of safe sex 1 1-5 n/a
Future intentions to binge drink 1 1-4 n/a
Future intentions to use illegal drugs 1 1-4 n/a
Sexual self-efficacy 6 1-4 .90
Family cohesion 6 1-4 .86
Social supporta4 0-4 .80
Spirituality 3 1-4 .85
Recidivism Time 2 1 0-1 n/a
Recidivism Time 3 1 0-1 n/a
a. These scales more reflect a count of occurrences, so we would not necessarily expect these scales to
follow traditional psychometric theory and have a high alpha (see Bollen & Lennox, 1991).
McKiernan et al. 105
participants who indicated they had only one sexual partner were assigned a value of
zero, even if they indicated having engaged in unprotected sex acts. This did not affect
the pattern of results, as no significant intervention effects emerged when analyzing
the data using this logically recoded variable or analyzing the data using the unrecoded
variable.
Perceived risk of risky sexual behavior (α = .82) was measured with three items
assessing the degree to with which participants thought people would risk harming
themselves if they engaged in risky sexual behaviors (e.g., if they share nonsanitized
needles/works when using drugs).
Participants responded on a 1 (not at all likely) to 4 (very likely) response scale,
where an additional response option was provided if they wished to indicate, “will
not do.”
Knowledge (α = .78) obtained during the intervention was assessed with 18 true/
false items (e.g., only people who look sick can spread the HIV/AIDS virus—false).
Knowledge was analyzed as the percentage of correct responses.
Future high likelihood of safe sex was assessed with one item where participants
indicated the likelihood that they would engage in safe sex in the next 6 months.
Participants responded on a 5, “will not do,” or 1 (not at all likely) to 4 (very likely)
response scale. Again, unprotected sex acts become less of a concern when partici-
pants are with a single and consistent sex partner. As such, participants who indicated
they were in a sexual relationship with only one partner were assigned a value of five.
This did not affect the pattern of results, as no significant intervention effects emerged
when analyzing the data using this logically recoded variable or analyzing the data
using the unrecoded variable.
Future intentions to binge drink and future intentions to use illegal drugs in the past
6 months were each assessed with single items where participants responded on a
1 (not at all likely) to 4 (very likely) response scale.
Sexual self-efficacy (α = .90) was assessed with six items assessing their comfort in
asserting their opinion in sexual situations (e.g., refuse to engage in sex practices you
didn’t like). Participants responded using a 1 (not at all) to 4 (very much) response
scale.
Family cohesion (α = .86) used six items (e.g., members of my family ask each
other for help) to assess whether there was a strong sense of attachment in the partici-
pant’s family. Participants responded on a 1, “no family,” or 2 (not true) to 5 (always
true) response scale.
Social support (α = .80) was measured with four items where participants indicated
whether they had persons available to talk to about life issues (i.e., sex, alcohol/drugs,
health, personal matters). A count of yes responses was calculated for analysis.
Spirituality (α = .85) was measured with three items (e.g., how spiritual or religious
would you say you are) using different Likert-type response scales. All items were
transformed to a one to four response scale prior to calculating the mean.
Recidivism was assessed by determining at waves two and three whether each par-
ticipant had a revocation, was arrested, or absconded. Recidivism data were provided
106 Criminal Justice Policy Review 24(1)
directly from the Department of Corrections for each participant, and these data were
not collected using the questionnaire.
Analysis. Our primary analysis of interest is concerned with examining whether (a)
the changes in the intervention group between waves one and three were more positive
than the changes in the comparison group between waves one and three, and (b)
whether changes in the intervention group were predicted by intervention dosage.
Thus this design reflects a quasi-experimental or correlational research design.
HLM was used to deal with multiple observations being nested within each partici-
pant (i.e., multiple wave repeated observations) for nearly all analyses for Question 1.
Although simpler general linear models can be used to handle these data, HLM per-
formed in this manner confers the benefits of being able to use all of the data, regard-
less of whether a participant has all three repeated observations (cf. Raudenbush &
Bryk, 2002). This method is more consistent with an intent-to-treat approach. All
models were posed as random intercept models, which assume that variability may
arise among individuals due to nesting. More specifically, at Level 1 (i.e., the repeated
observation level), all outcomes were seen as being predicted by orthogonally coded
linear (–1, 0, 1) and quadratic (1, –2, 1) time contrasts:
Outcome = π0 + π1(Linear) + π2(Quadratic)
At Level 2 (i.e., the individual level), the Level 1 intercept was seen as being pre-
dicted by a coded contrast (–1 vs. 1) representing the intervention group and our cor-
rection for selectivity biases due to attrition:
π0 = β00 + β01(Intervention) + β02(Inverse Mill’s Ratio) + r0
The remaining Level 2 equations represented the cross-level interactions between
time and intervention group:
π1 = β10 + β11(Intervention)
π2 = β20 + β21(Intervention)
This approach was used to examine antisocial outcomes; however, our criminal
outcome, recidivism, was examined using a simple, multiple logistic regression model.
These models regressed recidivism status at Times 2 and 3 in separate analyses on
intervention status and our correction for selectivity biases. All models were run using
SPSS 18.0.
Results. In the interest of brevity, only statistically significant findings are graphed
and discussed in the prose of the report. Our analysis of intervention effects suggested
that there were some antisocial outcome intervention effects for knowledge, future
intentions to binge drink, and spirituality, as well as intervention effects on recidivism
(i.e., criminal behavior) at wave three. The cell means/percentages for these effects
McKiernan et al. 107
appear in Table 2, and a summary of the statistical models appears in Table 3 for the
antisocial outcomes and Table 4 for the criminal outcomes. As can be seen in Tables 2
and 3, knowledge exhibited a larger increase between Times 1 and 3 in the intervention
group, relative to the change between Times 1 and 3 in the comparison group. Thus this
suggests that the intervention did inform participants about sexually transmitted dis-
eases, and these knowledge gains persisted between program exit and follow-up.
Changes were also observed for future intentions to binge drink, which increased over
time in the comparison group but remained relatively constant in the intervention group.
Spirituality tended to decrease in the comparison group, but spirituality increased in the
intervention group. As can be seen in Table 4, there was no evidence that the program
had a statistically significant effect on reducing recidivism by program exit; however,
at follow-up there was evidence to suggest that program participants were 3.7 times
more likely than comparison participants not to recidivate.
Whereas Study 1 suggests positive program effects on outcomes such as antiso-
cial behavior and recidivism, it does not speak to the relationship skills that the
Table 2. Unadjusted Study Cell Means and Percentages for Outcomes
Comparison Intervention
Time 1 Time 2 Time 3 Time 1 Time 2 Time 3
nmax 96 77 75 226 157 155
No. of days cigarettes used (in past 30) 20.65 20.03 16.46 19.22 16.40 15.11
No. of days other tobacco used
(in past 30)
5.15 6.88 6.68 7.46 7.67 6.25
No. of days alcohol used (in past 30) .48 .40 1.39 .40 .69 .70
No. of days drunk (in past 30) .47 .14 .68 .17 .53 .37
No. of days marijuana used (in past 30) .57 .14 1.33 .54 .15 .29
No. of days other illegal drugs used
(in past 30)
.16 .26 .75 .38 .45 .39
Perceived great risk of substance use 3.19 3.27 3.29 3.32 3.45 3.47
No. of types of unprotected sex acts
(last time)
1.43 1.38 1.24 1.47 1.53 1.53
No. of types of risky sexual behaviors
(past 3 months)
.32 .31 .27 .35 .32 .25
Perceived risk of risky sexual behavior 3.50 3.49 3.45 3.44 3.50 3.45
Knowledge 56.77 60.97 58.59 56.56 67.23 66.92
Future high likelihood of safe sex 3.38 3.41 3.51 3.19 3.31 3.36
Future intentions to binge drink 1.14 1.17 1.32 1.18 1.20 1.17
Future intentions to use illegal drugs 1.11 1.15 1.29 1.13 1.15 1.17
Sexual self-efficacy 2.85 3.03 2.98 2.91 3.08 2.92
Family cohesion 4.10 4.00 4.00 3.80 3.88 3.84
Social support 3.81 3.71 3.65 3.73 3.77 3.74
Spirituality 3.12 3.05 3.01 3.08 3.13 3.19
Recidivism Time 2 (%) 14.58 — — 13.72 — —
Recidivism Time 3 (%) 17.71 — — 6.64 — —
108 Criminal Justice Policy Review 24(1)
Table 3. Intervention Effect Unstandardized Regression Coefficients, Effect Sizes, and
Statistical Significance
Intercept
Attrition
Selectivity
Correction
Linear
Change
Quadratic
(U-Shaped)
Change Intervention
Intervention
X Linear
Intervention
X Quadratic
No. of days cigarettes
used (in past 30)
19.87 (.63)** –1.04 (–.07) –1.56 (–.16)** –.26 (–.05) –.75 (–.06) .26 (.03) .38 (.07)
No. of days other
tobacco used (in
past 30)
8.09 (.38)** –.87 (–.07) .17 (.02) –.26 (–.05) .29 (.03) –.80 (–.08)+.00 (.00)
No. of days alcohol
used (in past 30)
.75 (.20)** –.05 (–.02) .31 (.09)* .06 (.03) –.09 (–.04) –.15 (–.05) –.11 (–.06)
No. of days drunk (in
past 30)
.42 (.09)* –.02 (–.01) .10 (.03) .03 (.02) –.04 (–.01) .00 (.00) –.12 (–.06)+
No. of days marijuana
used (in past 30)
.56 (.17)* –.04 (–.02) .13 (.04) .18 (.10)* –.18 (–.10) –.25 (–.08)+–.09 (–.05)
No. of days other
illegal drugs used (in
past 30)
.48 (.15)* –.06 (–.03) .15 (.06) .02 (.01) .00 (.00) –.15 (–.05) –.04 (–.03)
Perceived great risk of
substance use
3.32 (.94)** .01 (.01) .07 (.11)* –.01 (–.04) .08 (.12)* .01 (.02) .00 (–.01)
No. of types of
unprotected sex acts
(last time)
1.42 (.59)** .00 (.00) –.03 (–.03) –.01 (–.01) .06 (.06) .04 (.05) .01 (.01)
No. of types of risky
sexual behaviors
(past 3 months)
.32 (.36)** .00 (.00) –.03 (–.05) –.01 (–.03) .00 (.00) –.01 (–.02) .00 (–.01)
Perceived risk of risky
sexual behavior
3.47 (.96)** –.01 (–.01) –.01 (–.03) –.01 (–.04) .00 (.00) .02 (.05) –.01 (–.03)
Knowledge 59.96 (.85)** 1.02 (.04) 3.18 (.22)** –1.38 (–.17)** 2.63 (.13)* 2.16 (.15)** –.51 (–.06)
Future high likelihood
of safe sex
3.30 (.86)** .06 (.04) .08 (.07) .00 (–.01) –.05 (–.05) .02 (.02) –.01 (–.02)
Future intentions to
binge drink
1.20 (.83)** .00 (.00) .05 (.08)+.01 (.02) –.02 (–.04) –.05 (–.09)* –.01 (–.03)
Future intentions to
use illegal drugs
1.20 (.82)** –.02 (–.04) .06 (.11)* .01 (.03) –.02 (–.04) –.04 (–.07)+–.01 (–.03)
Sexual self–efficacy 3.00 (.86)** –.03 (–.02) .04 (.05) –.04 (–.10)* .02 (.02) –.03 (–.03) –.01 (–.03)
Family cohesion 3.92 (.96)** .00 (.00) –.02 (–.04) .00 (.00) –.09 (–.14)* .04 (.07)+–.02 (–.06)
Social support 3.75 (.94)** .00 (.00) –.03 (–.05) .00 (–.01) .02 (.02) .05 (.07) –.01 (–.03)
Spirituality 3.03 (.92)** .03 (.04) –.02 (–.04) .01 (.03) .04 (.05) .05 (.13)** .00 (.00)
Note: Unstandardized regression coefficients listed first, and in parentheses t-values with accompanying degrees of
freedom were transformed to an effect size r, using the formula presented in Cohen (1988).
+p < .01. *p < .05. **p < .01.
program attempts to foster in participants. Study 2 was designed explicitly to address
this limitation.
Study 2
Method
Participants. The participants for the present study were 500 male individuals who
voluntarily participated in the CLFC program (i.e., intervention group) or one of the
McKiernan et al. 109
Table 4. Intervention Recidivism Effect Unstandardized Regression Coefficients, Odds
Ratios, and Statistical Significance
c Intercept
Attrition Selectivity
Correction Intervention
Recidivism Time 2 –.61 (.55) –.75 (.47) –.21 (.81)
Recidivism Time 3 –1.00 (.37)+–.23 (.80) –1.33 (.27)**
Note: Unstandardized coefficients come first and odds ratios appear in parentheses.
+p < .01. *p < .05. **p < .01.
programs typically offered for those being released from prison (i.e., the comparison
group). It is important to note that the majority of clients were released from prison at
the time of their participation (n = 389 or 78%); however, the remainder of the partici-
pants were still incarcerated at the time of their participation. Of the 500 clients, 387
participated in the intervention condition and 113 participated in the comparison con-
dition. The clients were in their 30s (M = 33.85) and predominately White (62%) or
African American (36%), with very few Hispanic clients (2%) being represented in the
sample. Examining the background characteristics of these clients, about one quarter
lived with a relationship partner (25%), were independently housed (27%), and had
children living with them (27%); however, most clients reported having a child (77%).
Most clients had a high school diploma or a GED (94%); however, less than half
(43%) were employed.
Selectivity Biases. As in Study 1, two alternative explanations for putative study find-
ings are that (a) intervention effects could be due to nonrandom assignment of indi-
viduals to the intervention and comparison groups (i.e., a quasi-experimental design)
and (b) intervention effects could be due to participants who are likely to exhibit nega-
tive outcomes being more likely to drop out of the study, especially in the intervention
group. Again, these potential sources of selectivity biases were addressed using a
Heckman two-step procedure (Heckman, 1976, 1979).
Our first probit model examined selectivity biases due to assignment to the inter-
vention or comparison group. Our model suggested that individuals who were Hispanic
were more likely to be in the comparison group, z = –2.12, p = .03; however, the over-
all model did not predict assignment, χ2(489) = 501.45, p = .34. As we did have one
significant predictor of assignment, we did produce an IMR representing selectivity
bias due to assignment. We performed our analyses initially including the IMR as a
covariate in all of our inferential models; however, it was not a statistically significant
predictor in any model (ps > .05). As such, all final models reported here excluded the
IMR as a predictor.
Our second probit model examined selectivity biases due to attrition. Considering
attrition, 136 clients (or 27%) did not participate at posttest or follow-up. Of the 500
participants at pretest, 385 (or 77%) participated at posttest and 364 (or 73%) partici-
pated at follow-up. There was no evidence to suggest than any of the background
110 Criminal Justice Policy Review 24(1)
characteristics predicted attrition (ps > .05) and the overall model did not predict attri-
tion, χ2(489) = 498.37, p = .38. As there was no evidence of bias due to attrition, we
did not create an IMR representing this source of selectivity bias.
Procedure. The procedures were identical in all respects to the procedures reported
for Study 1.
Measures
Questionnaire. Clients completed a questionnaire at each of the three waves of the
study that included 71 items inquiring about various relationship skills using a 1
(strongly disagree) to 5 (strongly agree) scale. Some of the relationship skill items,
developed by McGuire and Associates for this project, were adapted from scales by
Olson and colleagues (Barnes & Olson, 2003; Olson, 2006; Olson, Fournier, & Druckman,
1986; Olson & Schaefer, 2000) to more closely align with the content and principles
of CLFC. Nine facets of relationship skills were assumed to be measured by these
items. We examined whether all items purported to measure an underlying construct
were measuring the same underlying construct by calculating Cronbach’s alpha at
time one for each scale. Scale scores were calculated by taking the average of responses
to items comprising each scale. The psychometric properties of these measures appear
in Table 5. The nine scales measured in the data with example item content were as
follows.
• Communication Skills (α = .78, n items = 8). Example item: I am able to
express my true feelings to those whom I trust.
• Conflict Resolution Skills (α = .52, n items = 6). Example item: Even when
in a conflict with someone I trust, I can respectfully share my thoughts and
feelings.
• Intrapersonal Skills (α = .66, n items = 9). Example item: I am honest with
myself about what I feel and need.
Table 5. Psychometrics for Outcome Measures
No. of Items Range Alpha Time 1
Communication skills 8 1-5 .78
Conflict resolution skills 6 1-5 .52
Intrapersonal skills 9 1-5 .66
Emotional awareness 9 1-5 .78
Emotional expression 9 1-5 .85
Interpersonal skills 8 1-5 .80
Relationship management skills 8 1-5 .59
Relationship satisfaction 7 1-5 .89
Relationship commitment 7 1-5 .77
Recidivism Time 2 1 0-1 n/a
Recidivism Time 3 1 0-1 n/a
McKiernan et al. 111
• Emotional Awareness (α = .78, n items = 9). Example item: Those I trust
can really understand my hurts and joys.
• Emotional Expression (α = .85, n items = 9). Example item: I often let oth-
ers know what I am feeling.
• Interpersonal Skills (α = .80, n items = 8). Example item: I’m open and
honest with what I say to those I trust.
• Relationship Management Skills (α = .59, n items = 8). Example item: I
know I can count on some of the people in my life.
• Relationship Satisfaction (α = .89, n items = 7). Example item: I am happy
with how conflict is resolved in my relationships.
• Relationship Commitment (α = .77, n items = 7). Example item: I trust my
partner enough to stay with them.
Alphas were low for the Conflict Resolution Skills and Relationship Management
scales; however, alphas were acceptable for the remainder of the scales. The two
problem scales were not easily remedied, as alpha was not substantially improved by
dropping a small number of items. As such, findings for these two scales should be
interpreted with caution.
Preliminary examination of the data indicated that these nine relationship skills
were highly correlated at each wave. We performed a factor analysis at each wave
using principal axis factoring to determine whether all of these relationship skills
loaded on a single relationship skills factor. This was indeed the case, as all loadings
were greater than .49 for the factor analysis at each time period. Furthermore, alphas
were high at pretest (.91), posttest (.92), and follow-up (.93). As such, we created a
relationship skills aggregate, which serves as a summary measure for all of the rela-
tionship skills examined.
Recidivism was assessed by determining at waves two and three whether each par-
ticipant had a revocation, was arrested, or absconded. Recidivism data were provided
directly from the Department of Corrections for each participant, and these data were
not collected using the questionnaire. Recidivism data were only available for the 389
participants who were not currently incarcerated.
Analysis. Our primary analysis of interest is concerned with examining whether the
changes in the intervention group between waves one and three were more positive
than the changes in the comparison group between waves one and three. Thus this
design reflects a quasi-experimental or correlational research design.
HLM was used to deal with multiple observations being nested within each partici-
pant (i.e., multiple wave repeated observations) for nearly all analyses. All models
were posed as random intercept models, which assume that variability may arise
among individuals due to nesting. More specifically, at Level 1 (i.e., the repeated
observation level), all outcomes were seen as being predicted by orthogonally coded
linear (–1, 0, 1) and quadratic (1, –2, 1) time contrasts:
Outcome = π0 + π1(Linear) + π2(Quadratic)
112 Criminal Justice Policy Review 24(1)
At Level 2 (i.e., the individual level), the Level 1 intercept was seen as being pre-
dicted by a coded contrast (–1 vs. 1) representing the intervention group:
π0 = β00 + β01(Intervention) + r0
The remaining Level 2 equations represented the cross-level interactions between
time and intervention group:
π1 = β10 + β11(Intervention)
π2 = β20 + β21(Intervention)
This approach was used to examine relationship skills; however, recidivism was
examined using a simple, multiple logistic regression model. These logistic regression
models regressed recidivism status at Times 2 and 3 in separate analyses on interven-
tion status. All models were run using SPSS 18.0.
Results
Relationship Skills. We first examined the pattern of means for relationship skills by
condition and wave, which appears in Table 6. As can be seen in the table, the pattern
of changes in means by condition for most scales is similar. The contrast of changes in
the intervention and comparison groups appears in Table 7. Statistically significant
effects of particular interest appear in the columns 5 and 6 (i.e., Intervention X Linear
Table 6. Unadjusted Study Cell Means and Percentages for Outcomes
Intervention Comparison
Time 1 Time 2 Time 3 Time 1 Time 2 Time 3
N387 303 302 113 100 87
Communication skills 3.87 4.33 4.36 4.06 4.12 4.03
Conflict resolution skills 2.98 3.21 3.34 3.14 3.12 3.12
Intrapersonal skills 3.13 3.52 3.58 3.19 3.30 3.21
Emotional awareness 3.42 3.94 4.02 3.54 3.70 3.61
Emotional expression 3.59 4.21 4.26 3.73 3.87 3.86
Interpersonal skills 3.58 4.10 4.14 3.73 3.79 3.78
Relationship management skills 3.65 3.98 4.02 3.75 3.72 3.72
Relationship satisfaction 3.53 4.11 4.20 3.68 3.82 3.80
Relationship commitment 4.12 4.49 4.48 4.21 4.27 4.25
Relationship skills (avg. of 9 prior skills) 3.54 3.99 4.05 3.67 3.75 3.71
Recidivism Time 2 (%) 13.97 — — 14.86 — —
Recidivism Time 3 (%) 5.08 — — 13.51 — —
McKiernan et al. 113
and Intervention X Quadratic) of Table 7. The findings for the individual scales and
the aggregate relationship skills scale appear in both tables. Findings were in the same
direction for all scales; however, the Intervention X Quadratic interaction failed to
reach a conventional level of significance for Conflict Resolution Skills and Intraper-
sonal Skills. As all findings were in the same direction and the majority was signifi-
cant, we only interpreted the Relationship Skills aggregate in the interest of brevity.
The general pattern of results suggested that relationship skills remained relatively
constant for the comparison group; however, relationship skills improved for the inter-
vention group. More specifically, relationship skills exhibited a large increase between
pre- and posttest for the intervention group; and the level of relationship skills remained
high and stable between posttest and follow-up for the intervention group.
Recidivism. Examining recidivism, there were no differences between the interven-
tion and comparison group on recidivism between pre- and posttest; however, as can
be seen in Table 8, there was a significant difference between the intervention and
comparison group in recidivism between posttest and follow-up. This difference sug-
gested that clients in the comparison group were 2.94 times (or the inverse of the .34
odds ratio in Table 8) more likely to recidivate than clients in the intervention group.
Table 7. Intervention Effect Unstandardized Regression Coefficients, Effect Sizes, and
Statistical Significance
Intercept
Linear
Change
Quadratic
(U-Shaped)
Change Intervention
Intervention
X Linear
Intervention X
Quadratic
Communication
skills
4.13 (.99)** .12 (.22)** –.05 (–.16)** .06 (.12)* .13 (.24)** –.03 (–.09)*
Conflict resolution
skills
3.15 (.99)** .09 (.16)** –.01 (–.03) .02 (.04) .09 (.18)** –.01 (–.04)
Intrapersonal skills 3.32 (.99)** .12 (.23)** –.04 (–.15)** .09 (.18)** .11 (.21)** –.01 (–.04)
Emotional
awareness
3.70 (.99)** .17 (.33)** –.05 (–.19)** .09 (.16)** .13 (.25)** –.02 (–.07)*
Emotional
expression
3.92 (.99)** .20 (.35)** –.06 (–.19)** .10 (.17)** .14 (.24)** –.04 (–.12)**
Interpersonal skills 3.86 (.99)** .16 (.30)** –.04 (–.15)** .08 (.15)** .12 (.24)** –.04 (–.13)**
Relationship
management
skills
3.81 (.99)** .09 (.20)** –.02 (–.09)* .08 (.16)** .10 (.23)** –.03 (–.12)**
Relationship
satisfaction
3.85 (.98)** .20 (.29)** –.05 (–.14)** .09 (.13)** .14 (.21)** –.03 (–.08)*
Relationship
commitment
4.30 (.99)** .10 (.20)** –.04 (–.14)** .06 (.13)** .08 (.16)** –.03 (–.09)*
Relationship skills
(avg. of 9 prior
skills)
3.78 (.99)** .14 (.34)** –.04 (–.19)** .08 (.17)** .11 (.29)** –.02 (–.12)**
Note: Unstandardized regression coefficients listed first, and in parentheses t-values with accompanying degrees of
freedom were transformed to an effect size r, using the formula presented in Cohen (1988).
+p < .10. *p < .05. **p < .01.
114 Criminal Justice Policy Review 24(1)
Conclusion
The outcomes of this research indicate substantial improvements in all areas of inves-
tigation through producing gains in relationship skills, reductions in substance use, and
recidivism. Like other studies, these two studies indicate that building meaningful
relationships with offenders and implementing evidence-based interventions increases
strengths and reduces risk behavior. These results reflect the findings in other studies
that demonstrate the importance of the therapeutic alliance. Substance abuse treatment
compliance and retention studies have identified that program attributes that increase
engagement in treatment improve treatment outcomes (Barber et al., 2001; De-Weert-
Van, Schippers, DeJong, &Schrijvers, 2001; Simpson, 2004; Simpson, Joe, & Rowan-
Szal, 2001). Consistent with findings from Corrigan and Bogner (2007), individuals
who stay in treatment longer are not only more likely to achieve sobriety but also to
develop new behavior and sources of reinforcement that serve to maintain sobriety.
Furthermore, studies on success in treatment identify the positive role of motiva-
tion and engagement. Studies indicate that clients with high motivation are more likely
than those with low motivation to become actively involved in treatment, to complete
the prescribed course of treatment, and to have better outcomes following treatment
(Huebner & Cobbina, 2007). It is notable that an adjunctive component to the CLFC
program, referred to as the Joint Intervention Meeting (JIM), provides a combination
of characteristics of the therapeutic alliance (e.g., the meeting is designed to connect
with the client) and aligned with methods to increase and sustain client motivation (the
meeting focuses on what is important to the client; Knight, Hiller, Broome, & Simpson,
2000). The utilization of this approach influenced retention and completion, meriting
further study.
Why Does CLFC Work With This Population?
The results clearly indicate positive outcomes for participants in the CLFC program.
Examination of the CLFC program identified a variety of mediators associated with
success described in the substance abuse treatment literature. CLFC contains and
delivers interventions that increase coping skills and motivation to change, improve
self regulation, and encourage the creation of a social support network. This support
network promotes prosocial behaviors and provides ongoing accountability. Notably,
Table 8. Intervention Recidivism Effect Unstandardized Regression Coefficients, Odds
Ratios, and Statistical Significance
Intercept Intervention
Recidivism Time 2 –1.75 (.17)** –.07 (.93)
Recidivism Time 3 –1.86 (.16)** –1.07 (.34)*
Note: Unstandardized coefficients come first and odds ratios appear in parentheses.
+p < .10. *p < .05. **p < .01.
McKiernan et al. 115
the CLFC program interventions effectively reduce stress and other negative factors
associated with relapse, including negative self-talk. The program staff also imple-
ment techniques associated with a strong therapeutic alliance that include elements of
motivational interviewing (e.g., offering a menu of options). Finally, to ensure opti-
mal performance and consistency of programming, staff and management team mem-
bers regularly engage in fidelity checks. While the theoretical framework and program
concepts reflect activities previously reported in the literature, the comprehensive
coordination of these activities among partner agencies distinguishes the program’s
uniqueness. More specifically, from the very first meeting, each participant is greeted
with genuine concern and respect. Regardless of what the participant does or how they
act, all staff members are trained to respond with respect and positive regard. This
approach appears to be perceived as something new and different for the typical
criminal offender. This unconditional positive regard strengthens the relationship and
promotes bonding with staff and other participating peers. Along these lines, expecta-
tions for participant behavior are clearly communicated and maintained in a firm but
caring manner. This approach also appears to be perceived as something new and
different for the average participant in this study. Furthermore, those participants who
demonstrate an inability to follow the mutually agreed-on program expectations are
invited to experience a kind and thoughtful Joint Intervention Meeting (JIM described
earlier). The JIM utilizes elements of evidence-based interventions reported in the
family treatment of addictions literature (e.g., avoiding expressions of anger, stating
the facts regarding behavior, and encouraging positive change). Combined with the
consistency of the staff’s respectful approach, the JIM proves to be something new,
different, and powerful for these reentry participants.
Why does this intervention excel at retention? The consistent treatment of each
participant with respect and unconditional positive regard appears to produce a feeling
of being wanted, welcomed, and cared about that engenders a desire by participants to
keep attending. The COPES staff members are trained to focus on and express high
expectations for positive change. Each client’s positive movement, no matter how
small, receives recognition and support.
How does this intervention produce better outcomes? The CLFC program contains
elements identified in the substance abuse treatment literature associated with
improved outcomes including increases in coping skills, motivation, self-efficacy,
accountability, feedback, and peer support as well as reducing self-defeating behavior
including isolation. The content, while not unique or unavailable through other
sources, is significantly strengthened by the method of delivery. The COPES staff
trains extensively to ensure fidelity of each individual program session and overall
program service delivery. Within this framework of fidelity program implementation,
the COPES staff collaborated and communicated closely with KDOC and other part-
ners to ensure consistency. The consistency of the staff and community partners rein-
forces the importance of the content, the value of each individual participant, and the
community’s investment in each participant’s success.
116 Criminal Justice Policy Review 24(1)
In summary, the Creating Lasting Family Connections (CLFC) program is a com-
bination of strong therapeutic alliance coupled with the implementation of a unique
blend of evidence-based practices in an intervention delivered with fidelity and rein-
forced over time. This process, previously described as “connect-immunity,” empow-
ers individuals to first recognize and accept their personal and family responsibility,
and to ultimately develop a deeper recognition of both what they contribute to and
receive from the larger community. This represents a true model of what has been
previously described as placing importance on the individual’s well-being as a means
of achieving community safety. As a result of this programming, many participants
remained in the community and the community remained safe.
These combined positive outcomes endorse the importance of continued imple-
mentation and expansion of community-based agencies delivering evidence-based
interventions to reentry populations. The outcomes also strongly endorse the consider-
ation of the following recommendations:
1. Consider evidence-based family strengthening programming with reentry
populations to reduce recidivism.
2. Examine the mechanism of action within evidence-based practices to
increase understanding of how they work with reentry populations.
3. Increase movement toward the policy of connecting reentry populations with
community-based organizations trained in evidence-based approaches and
cultural awareness with reentry populations, as these two studies show this
approach produces positive results.
4. Recognize the importance of programming of significant scope and duration
in producing lasting change.
5. Look at cost-effective methods to provide long-term support for reentry
populations, and consider the use of technologies such as web-based and cell
phone applications to increase opportunities for low-cost and longer term
reentry support services.
6. Recognize and endorse the role of interagency collaboration to ensure a unified
approach and consistency in programming provided for reentry populations.
The clear limitation of these studies is that firm conclusions are precluded by both
(a) the results being based on a quasi-experimental design and (b) a lack of explication
of the underlying mechanisms by which the positive outcomes are produced by the
program. The former concern is less troublesome, as the reported studies were not
based on a purely convenience sample of participants. Also, the robust nature of our
findings, especially for recidivism, helps foster faith that the program, as opposed to
selectivity biases, produced the observed results. Furthermore, explicit statistical con-
trols were included in our models for such biases. The latter, while it does not impugn
the positive program effects on outcomes, underscores the need for future research to
explore the causal mechanisms by which the CLFC program works. On balance, these
McKiernan et al. 117
preliminary investigations suggest the CLFC program is a promising and effective
tool to aid the prison systems in rehabilitation for reentry populations.
Acknowledgments
The authors would also like to acknowledge deep appreciation for the following individuals and
their respective organizations for their profound contributions to the collaborative efforts in
achieving the results reported in this article: Kevin Pangburn, Gwen Holder, and Paulette
Wachtel, Kentucky Department of Corrections; Robert Lanning, Mark Meredith, and Ronyal
Horton, Dismas Charities, Inc.; Carl Powell, Richard Coomer, Kathy Kleier-Coates, and Tina
Haley, Volunteers of America of Kentucky; Capt. Florentino Merced-Galindez, CSAP; Geneva
Ware-Rice, Charles M. Sutton, PhD, and Phyllis Carrol, OFA; Craig McGuire, McGuire and
Associates; and all members of the COPES staff and Project Advisory Group (PAG). It was
only through the power of collaboration that such results became possible.
Authors’ Note
This Criminal Justice Policy Review article was developed, in part, under grant number
SP13365 from the Substance Abuse and Mental Health Services Administration (SAMHSA),
and, in part, under grant number 90FR0015 from the Administration for Children and Families
(ACF). The views, opinions and content of this publication are those of the authors and con-
tributors, and do not necessarily reflect the views, opinions, or policies of SAMHSA, ACF or
the US Department of Health and Human Services (HHS), and should not be construed as such.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of
this article.
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Bios
Patrick McKiernan is trained in the treatment of addictions has a PhD in Psychology with
thirty years experience working in the counseling field. Throughout his career he has developed
and directed addiction treatment programs for criminal offenders, veterans, and adolescents.
His interest includes developing transitional housing, employment and training, and use of
technologies to improve addition treatment outcomes.
Stephen R. Shamblen was trained as a social psychologist and he has served as the statistical
investigator on many grants and contracts that have been conducted at the PIRE Louisville
center. His interests and first-author published work has focused largely on the efficacy of vari-
ous secondary data indicators as measures of need for prevention funding.
David A. Collins has been a researcher at PIRE since 2000. His research interests include
the study of protective factors related to substance abuse and other problem behaviors. He
has led evaluations of projects funded by the National Institute on Drug Abuse, the
Substance Abuse and Mental Health Services Administration, and the U.S. Department of
Education.
Ted N. Strader, MS, CPS, is Executive Director of COPES, Inc. and has written, managed and
served as Project Director of several federal grant projects. Mr. Strader has developed a number
of evidence-based substance abuse prevention, fatherhood, and marriage curricula listed on the
National Registry of Evidence-based Programs and Practices (NREPP) and has served as a
consultant to innumerable public and private organizations. A 4-time National Prevention
Network (NPN) Exemplary Program Award winner, Mr. Strader has published several books,
and professional articles and has presented keynote addresses, seminars and workshops at
numerous local, state and national conferences.
Christopher Kokoski is a coordinator and trainer/facilitator at COPES, Inc. Mr. Kokoski has
presented on a wide range of topics both locally and nationally on a wide range of topics. Mr.
Kokoski is also a Certified Prevention Specialist (CPS) and Certified Master Trainer of the
Creating Lasting Family Connections (CLFC) curriculum.