Is Risk-Need-Responsivity Enough? Examining Differences in Treatment Response Among Male Incarcerated Persons

• Date: 01 July 2020
• Published date: 01 July 2020
• Author: Riley Davis,Guy Bourgon,Ashley B. Batastini,Michael E. Lester
• DOI: 10.1177/0093854820915740
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2020, Vol. 47, No. 7, July 2020, 829 –847.

DOI: https://doi.org/10.1177/0093854820915740

Article reuse guidelines: sagepub.com/journals-permissions

© 2020 International Association for Correctional and Forensic Psychology

829

IS RISK-NEED-RESPONSIVITY ENOUGH?

Examining Differences in Treatment Response Among

Male Incarcerated Persons

MICHAEL E. LESTER

ASHLEY B. BATASTINI

RILEY DAVIS

The University of Southern Mississippi

GUY BOURGON

Corrections Research, Public Safety and Emergency Preparedness Canada

Research examining the efficacy of cognitive behavioral therapy (CBT) in reducing recidivism has paid little attention to

treatment factors contributing to response variability. Using an archival sample of 448 participants exposed to a risk-need-

responsivity (RNR)-informed CBT program or no treatment, a multigroup latent profile analysis yielded a four-profile solu-

tion: a treatment-nonresponsive group and three treatment-responsive groups. Among the treatment-responsive profiles,

reduced criminal attitudes were most predictive of desistance from reoffending. Elevated rates of recidivism and negligible

gains following treatment were associated with pretreatment elevations in antisocial traits, risk level, and negative attitudes

toward treatment. These findings underscore a greater need for individualized assessment of risk and treatment motivation,

the importance of altering criminal sentiments to prevent reentry into the system upon release, and challenge the idea that

200 hours of treatment is sufficient for lasting change. Study limitations and further directions are discussed, including the

need for correctional treatment outcome research to better isolate individual differences.

Keywords: risk-need-responsivity; correctional treatment; recidivism; male justice-involved persons

Since 2000, the rate of growth for the world’s prison population has exceeded that of the

general population in many countries (Walmsley, 2018). The United States currently

has the largest prison population with approximately 655 per 100,000 citizens incarcerated

(Walmsley, 2018). In contrast, Canada incarcerates 136 individuals per 100,000 citizens

(Bonta et al., 2003; Malakieh, 2018). As almost all of those who are incarcerated eventually

return to their communities (Seiter & Kadela, 2003), 68% of whom are likely to

AUTHORS’ NOTE: Correspondence concerning this article should be addressed to Ashley B. Batastini,

School of Psychology, The University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406;

e-mail: Ashley.Batastini@usm.edu.

915740CJBXXX10.1177/0093854820915740Criminal Justice and BehaviorLester et al. / Is Risk-Need-Responsivity Enough?

research-article2020

830 CRIMINAL JUSTICE AND BEHAVIOR

be rearrested within 3 years of their release (Alper et al., 2018), evaluations of treatment

efficacy need to provide clinicians with a better understanding of how to tailor interventions

to individual needs so returning citizens do not return to prison.

Many in the field acknowledge the risk-need-responsivity (RNR) model as one of the

most widely accepted approaches to treatment (e.g., Morgan et al., 2012). According to

RNR, interventions should be more intensive for higher risk individuals than lower risk

individuals (Risk), should address empirically supported dynamic risk factors (i.e., crimi-

nogenic needs) associated with continued criminal behavior (Need), and should be deliv-

ered in a manner consistent with individuals’ developmental, cognitive, or physical abilities

and resources (Responsivity; Bonta & Andrews, 2007). Under this model, risk level and

needs are determined through the assessment of the eight factors most predictive of recidi-

vism. Criminal history, antisocial personality traits, criminal cognitions, and antisocial

associates are the most predictive risk factors for recidivism and are commonly termed the

“Big Four” (Andrews et al., 2006). Several studies and meta-analyses have shown that

interventions using an RNR-informed model can reduce recidivism, with reductions in reof-

fense rates as high as 30% (Bourgon & Armstrong, 2005; Morgan et al., 2012).

There are several factors, however, that are not explicitly accounted for by RNR that may

impact responsiveness to treatment. Two such factors are the need to establish initial inter-

vention buy-in (Rosen et al., 2004) and the need to address mental health risks when rele-

vant (Morgan et al., 2012). While the presence of psychological symptoms alone is not a

risk factor for crime among the general population (Skeem et al., 2015), the prevalence of

mental health issues is higher for incarcerated than nonincarcerated populations (Bronson

& Berzofsky, 2017; Prins, 2014). A U.S. Bureau of Justice Statistics report (Bronson &

Berzofsky, 2017) estimated that 36.9% to 44.3% of persons in custody have a history of

mental health disorder with depression accounting for the majority of serious mental illness.

The applicability of RNR to individuals with co-occurring clinical needs is at-present

unclear (Skeem et al., 2015), yet vital for understanding the breadth of impact of RNR-

based programs for persons in custody as a group. Many of the same established techniques

for addressing the criminogenic side of risk, including cognitive behavioral therapy (CBT)

interventions (Wooditch et al., 2014) and social learning methods (e.g., prosocial modeling;

Bonta & Andrews, 2007), can incorporate mental health concerns when necessary. In fact,

the use of social learning and CBT for both individuals with and without mental illness has

been well supported in the literature (Landenberger & Lipsey, 2005; Morgan et al., 2012).

Yet, because mental health is not directly emphasized in the RNR model, studies examining

RNR-informed interventions may be missing an important factor. Therefore, the inclusion

of clinical needs remains important as such needs may nonetheless contribute to variability

in responsiveness to treatment.

To our knowledge, studies evaluating the efficacy of RNR-informed CBT have almost

exclusively focused on overall treatment outcomes for the population of interest rather than

subgroups of individuals within the population. Persons in custody have been shown to be

heterogeneous in regard to both criminogenic and mental health needs (Mokros et al., 2015;

Sampson & Lauritsen, 1994). However, no known research has explored the variability of

treatment effects for persons in custody participating in a standardized RNR-informed CBT

program. The absence of research focusing on the influence of individual differences is

problematic, as the overreliance on average treatment effects may misrepresent the actual

effectiveness of RNR-informed programs (Kravitz et al., 2004). That is, programs shown to