Abstract
Research supports the effectiveness of the Risk-Needs-Responsivity model for reducing criminal recidivism. Yet programming interests of inmates—one facet of responsivity—remain an understudied phenomenon. In the present study, we explored the programming interests of 753 federal inmates housed across three levels of security. Results suggest that inmates, as a group, prefer specific programs over others, and that some of their interests may differ by security level. We discuss possible implications of these findings.
The impact of programming on criminal recidivism has been examined in more than 60 meta-analyses during the past quarter century (Andrews, 2012). These quantitative reviews of the literature generally support the effectiveness of the Risk-Needs-Responsivity (RNR) model for the reduction of recidivism (Andrews & Bonta, 2010). Programs adhering to the RNR model match intervention intensity to recidivism risk level of offenders, focus predominantly on criminogenic needs (i.e., factors associated with the development and persistence of criminal behavior) of offenders, and deliver services in a way that enhances offenders’ responsivity to interventions (Andrews & Bonta, 2006).
The third principle of the RNR model, responsivity, is based on the premise that service design and delivery influence offender initiation, attendance, engagement, rehabilitation material acquisition, program completion, and recidivism (e.g., Wormith & Olver, 2002). This third principle states that the most effective programs use interventions grounded in behavioral or cognitive-behavioral theory. It also states that the most effective programs take into account the unique abilities, learning styles, and motivation levels of offenders. Yet despite its apparent importance, the responsivity principle is supported by “very scattered” research (Andrews, 2012, p. 139). As such, attention to the interactions between offenders and programming elements stands as a “high priority issue” (Andrews, Bonta, & Wormith, 2006, p. 23).
Programs designed to reduce recidivism clearly differ in many respects from those designed to improve mental health (Magaletta & Verdeyen, 2005); however, studies of the latter nevertheless contain findings that shed light on variables that might influence responsivity (e.g., Diamond, Magaletta, Harzke, & Baxter, 2008; Morgan, Steffan, & Shaw, 2007). They identify, for example, variables associated with mental health-seeking behaviors in correctional settings: Inmates who seek mental health services during incarceration tend to have sought mental health services in the community before their incarceration, and they tend to present with positive help-seeking attitudes rather than mistrust of mental health professionals (Deane, Skogstad, & Williams, 1999; Howerton et al., 2007; Skogstad, Deane, & Spicer, 2005, 2006). Apparently no single psychological problem (e.g., relationship, emotional, health-related) drives help-seeking behavior more than any other problem, irrespective of security level, racial background, and amount of time served (Morgan, Rozycki, & Wilson, 2004).
A second line of research sheds light on offender characteristics that influence another outcome of interest—programming completion. Meta-analytic findings indicate responsivity indicators are among the strongest predictors of attrition in offender treatment programs (Olver, Stockdale, & Wormith, 2011). Indeed, the magnitude of the relationship between attrition and a single responsivity variable, negative treatment attitude, is markedly greater than any other attrition predictor previously found in the general psychological treatment literature (e.g., age, education level, personality disorder; Swift & Greenberg, 2012; Wierzbicki & Pekarik, 1993; also see Edlund et al., 2002). Moreover, recidivism risk level is positively associated with treatment attrition, suggesting that inmates with the greatest need for treatment are the least likely to complete it (also see Hanson et al., 2002; Nunes & Cortoni, 2006).
Attendance and completion of correctional programs may be enhanced by motivational interviewing (Zweben & Zuckoff, 2002), an approach to service delivery associated with positive treatment outcomes for a variety of problems (Burke, Arkowitz, & Menchola, 2003; Hettema & Hendricks, 2010; Jensen et al., 2011). Its “spirit” is rooted in a collaborative approach, whereby a “partner-like relationship” is created between the treatment-seeker and treatment provider (Miller & Rollnick, 2002, p. 34). This approach stands at-odds with the prevailing culture of some correctional facilities, where staff and inmates are viewed as adversaries, and where correctional plans rather than inmates dictate rehabilitation goals. Nevertheless, a collaborative approach is considered essential for effective treatment planning (Woody, Detweiler-Bedell, Teachman, & O’Hearn, 2003) and is suitable for offender populations (Ginsburg, Mann, Rotgers, & Weekes, 2002).
One way to demonstrate a collaborative approach to treatment programming with offenders—and hopefully increase their responsivity to programming—is to offer programs that interest them. However, we identified no study published within the past 50 years that specifically addressed inmate interest in programming. The current study represents an effort toward filling that gap. It was conducted to better understand programming interests of inmates—with respect to content, intensity, and duration—as a function of security level. In so doing, it adds to the research base of the RNR model by addressing an understudied principle, responsivity, within the context of its more salient principles (i.e., risk and needs).
Method
The present study took place at a federal correctional complex located in the U.S. South. It began when approximately 15 staff members with more than 150 combined years of correctional experience were appointed to a committee tasked with reducing inmate idleness and increasing inmate participation in programming. Committee members represented diverse aspects of correctional expertise ranging from unit management and prison industries to education and psychology. Members of the committee reviewed the literature on effective correctional programming in an effort to identify a broad range of interventions that might reduce institutional misconduct and community recidivism. They also discussed other interventions that, based on their experience, might reduce misconduct and recidivism.
Next, the committee constructed an operational survey instrument containing programs known to target criminogenic needs, as well as programs the complex’s administrators had expressed specific interest in pursuing. The survey instrument is included in the appendix. As shown, programs were categorized into three broad domains: self-improvement, relationship improvement, and community improvement. The survey also contained items regarding preferred session length and program duration. As measured by Flesch–Kincaid, the reading level of the final survey was equivalent to a grade level of 8.0.
Correctional staff members distributed copies of the survey to inmates housed in the complex’s general population. When the survey was distributed, the complex held approximately 4,000 sentenced offenders at varying types of custody and levels of security. Approximately 300 were classified as out-custody, minimum security camp inmates. Approximately 2,000 were classified as in-custody, low security inmates. Approximately 1,700 were classified as in-custody, medium-security inmates. Roughly one half of the inmate population was Black, about one third of the inmate population was White, and most of the remainder of the inmate population was Hispanic.
Inmates from the general population were advised that the complex was seeking to gain an understanding of their programming interests, that participation in the survey was entirely voluntary, and that survey responses would remain anonymous. To further assure participants of their anonymity, pertinent data were represented categorically (e.g., age) or omitted from the survey entirely (e.g., race). Correctional staff members distributed the surveys, then collected and delivered them to the institution psychology department.
Operational staff members distributed the surveys to inmates in their housing units. We, the authors of the present article, had no control or oversight over survey distribution; we had no information pertaining to the number of surveys that actually were distributed, precluding us from calculating the survey non-response rate. In total, data from 753 completed surveys, a figure representing approximately 20% of the inmate population, were analyzed. Chi-squares were used to analyze categorical data. A standardized residual (SR), which is equivalent to a z-score, was used to analyze each contingency table cell.
Results
Sample characteristics are presented in Table 1. As shown, compared with their counterparts at the minimum and low security institutions, medium-security inmates were more likely to be young, with nearly 75% of the sample aged 40 or below (SR = −3.36). As well, medium-security inmates were more likely to be single (SR = 2.22). They were more likely to have extensive criminal histories, with a greater number of them having had a history of at least one apprehension as a juvenile (SR = 3.79), and a greater number of them having been arrested many times as an adult (i.e., 5-10 arrests, SR = 3.31; 11 or more arrests, SR = 3.11). As age, marital status, criminal history, and juvenile history are all associated with recidivism risk, results suggest medium-security inmates probably did, indeed, present a greater risk of recidivism than minimum and low security inmates. This supports the use of security levels as proxies for recidivism risk.
Demographics and SRs of Inmates Based on Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339). A positive SR indicates the observed frequency is greater than expected, whereas a negative SR indicates the observed frequency is less than expected). SR = standardized residual
Less than expected count for chi-square analysis.
p < .001. ****p < .0001.
Overall, a high proportion of the sample (n = 682; 90.7%) endorsed interest in at least one program offered in the survey. More inmates endorsed interest in programs related to self-improvement (n = 658, 87.4%) than relationship (n = 579, 76.9%) or community improvement (n = 464, 61.6%). Table 2 shows that inmates were most interested in specific programs offering opportunities to participate in volunteer activities, learn trade skills, enhance communication skills, and engage in postsecondary educational activities. Inmates were least interested in specific programs targeting substance abuse, marital dissatisfaction, impulse-control deficits, and aggression. With a few notable exceptions (e.g., trade skills, education), inmates preferred programs that were short in both session length (i.e., 1 hr per day) and overall duration (i.e., 1 to 3 months).
Programming Interest as a Function of Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339).
Tables 3 through 6 show the extent to which interest in programs varies as a function of inmate classification. Compared with their counterparts, minimum security out-custody camp inmates were more likely to endorse interest in volunteering; low security inmates were less likely to endorse interest in enhancing parenting skills; and medium-security inmates were more likely to endorse interest in programs that target communication skills, anger management, and substance abuse. As well, compared with low security inmates, medium-security inmates expressed greater interest in programs targeting relationship improvement.
Endorsed Relationship Improvement Programs Interest and SR by Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339). SR = standardized residual.
p < .001.
Endorsed Self-Improvement Programs Interest and SRs by Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339). SR = standardized residual.
p < 05. **p < .01. ***p < .001.
Endorsed Community Improvement Programs Interest and SRs by Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339). SR = standardized residual.
p < .01.
Endorsed Programming Interest and SRs by Security Classification.
Note. N = 753 (camp n = 64; low n = 350; medium n = 339). SR = standardized residual.
p < .001.
Discussion
The effectiveness of correctional programs likely will be enhanced by increased attention to the third principle of the RNR model, responsivity. One facet of this principle is the notion that inmates’ interests might influence their attitudes toward participating in correctional programs. Their interests also might influence their motivation to complete programs. Yet this kind of collaborative approach, in which inmates’ interests are considered in formulating rehabilitation goals, does not appear to be widely used by prison administrators when selecting programs, or by researchers in constructing program evaluations. Based on our review of the literature, the present study represents the only contemporary research available to guide administrators who are seeking to implement programs that are both empirically supported and likely to be of interest to inmates.
In the present study we found that, in general, inmates endorsed the greatest level of interest in programs involving volunteer activities, trade skills acquisition, communication or social skills enhancement, and postsecondary education. Inmates endorsed the lowest level of interest in programs targeting substance abuse, marital dissatisfaction, impulse-control deficits, and aggression. As a group, the sample appeared to be interested most in programs that were short in session length and overall duration.
Prison administrators, particularly those facing fiscal constraints, might consider data such as these when determining the nature, intensity, and duration of programs they offer to inmates. With a few notable exceptions, they might increase participation in interventions if they offer programs that are popular within their institutions, that require relatively little time per daily session, and that last no longer than 3 months in duration. They also might do well to offer incentives to inmates to complete programs that are unpopular but target important areas (e.g., substance abuse, impulse-control, anger, aggression). Attention to characteristics such as these might be particularly important for high-risk offenders, as research shows they are not as likely as low-risk offenders to complete treatment (e.g., Wormith & Olver, 2002).
Responsivity remains an understudied principle of the RNR model. The present research represents a modest attempt to address a single facet of responsivity, inmates’ programming interests. Our data indicate programming interests may vary as a function of classification level. These differences suggest that a “one-size-fits-all” approach to inmate programming is unlikely to be efficient. Rather, prison administrators might do well to consider the risk levels and interests of inmates at their specific institutions when deciding upon which empirically based programs they intend to offer.
Although the sample size in the present study was adequate, the generalizability of the results remains questionable. This is because the operational nature of the survey precluded random selection and prevented us from determining the non-response rate, and as such, we do not know how well these data apply to the greater inmate population (e.g., Armstrong & Overton, 1977; Baruch, 1999; Davern, 2013; Groves, 2006; Halbesleben & Whitman, 2013; Krosnick, 1999; Sivo, Saunders, Chang, & Jiang, 2006). As well, the sample was drawn from a single federal correctional complex in the South confining exclusively adult males. Therefore, additional research studies—with more complex designs, varied groups of offenders (e.g., juveniles, women), drawn from additional levels of security (e.g., high, supermax) and other regions of the country—is warranted.
These limitations notwithstanding, it is reasonable to infer that inmates are more likely to complete programming if they are interested in it. Therefore, surveying inmate populations about their interests can be prudent. This simple step may demonstrate administrators’ willingness to collaborate on important issues. It also may provide administrators with potentially valuable data about their population, enabling them to offer programs that will attract inmates. Finally, it can help administrators decide which programs, if any, might need incentives to draw more participants.
Footnotes
Appendix
Authors’ Notes
The views expressed in this article are those of the authors and do not necessarily reflect the views or opinions of any department, agency, or institution with which the authors are affiliated, including, but not limited to, the Department of Justice or Federal Bureau of Prisons.
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.