Core Correctional Practices in Community Supervision: An Evaluation of a Policy Mandate to Increase Probation Officer Use of Skills

Abstract

Community supervision officer training programs aim to translate core correctional practices into routine practice. These training programs emphasize skill-building designed to shift supervision strategies from law enforcement/compliance-oriented to a focus on promoting and supporting behavior change. Despite evidence of their effectiveness, research finds trained officers use newly learned skills infrequently. The current study examined the impact of a policy, implemented post-training, designed to encourage trained officers to use skills emphasized by the Staff Training Aimed at Reducing Rearrest (STARR) training program more frequently. The current study examined the effectiveness of this policy on the frequency and type of skills used by officers in their interactions with individuals on their caseload. Analyses suggested the policy mandate was effective in increasing skill use, however officers still used trained skills in less than half of their interactions. Implications and considerations for increasing the use of skills are discussed.

Keywords

correctional training program evidence-based practice implementation organizational change STARR probation

A large body of research cites the existence of competing goals of rehabilitation and punishment in correctional work (Klockars, 1972; Paparozzi & Gendreau, 2005; Skeem & Manchak, 2008). While originally implemented with an emphasis on social support, community corrections agencies evolved to focus on compliance and law enforcement responsibilities (Cullen & Gilbert, 1982; Taxman, 2002). Over the last several decades, however, there has been increasing attention toward the ineffectiveness of this punitively oriented system (Bonta et al., 2008) coupled with tight budgets and an over-burdened correctional system (Nagin et al., 2009). As a result, researchers and stakeholders began to emphasize the use of evidence-based practices (EBPs) or services that are supported by scientific research to improve outcomes (Latessa et al., 2020; MacKenzie, 2006; Sherman et al., 1998).

Research on EBPs in community corrections settings seeks to better align practices with interventions that are scientifically validated to improve supervision outcomes (Andrews et al., 1990; Bonta & Andrews, 2017; Dowden & Andrews, 2004; Gendreau, 1996). The use of best practices aims to shift the pendulum toward the rehabilitation goal of supervision and encourage practitioners to make decision that result in the provision of interventions for the appropriate populations, targeting the appropriate risk factors, and applied in the most effective ways (Andrews et al., 1990; Cullen & Gendreau, 2000). While supported by meta-analytic evidence (Andrews et al., 1990; Andrews & Dowden, 2006; Bonta & Andrews, 2017, Lipsey & Cullen, 2007), implementation of EBPs in correctional settings is challenging (e.g., Bonta et al., 2008; Viglione et al., 2015; Viglione, 2017). This is not surprising given the culture change required to fully embrace EBPs in traditionally authoritarian environments.

In response to the well-documented implementation challenges, researchers have developed community supervision officer training programs designed to help translate EBPs into routine practice (Chadwick et al., 2015; Gleicher et al., 2013). These training programs emphasize the development of knowledge and skill-building in several key areas designed to shift supervision strategies from law enforcement/compliance-oriented to a focus on promoting and supporting behavior change. Despite evidence of their effectiveness and ability to increase the application of the principles of effective intervention in practice (Bonta et al., 2011; Robinson et al., 2011; Smith et al., 2012), research finds that trained officers still use these practices at best 58% of the time (Bonta et al., 2011), with some studies noting use as low as 44% (Robinson et al., 2011). However, there has been very little examination of strategies agencies have implemented other than training and coaching (Labrecque & Smith, 2017) to increase the frequency of skill use. The current study sought to add to this body of research by examining the impact of a policy, implemented post-training, designed to encourage trained officers to use skills emphasized by the Staff Training Aimed at Reducing Rearrest (STARR) training program more frequently. The current study examined the effectiveness of this policy on the frequency and type of skills used by officers in their interactions with individuals on their caseload.

Evidence-Based Community Supervision Practices

Based on the general personality and cognitive learning (GPCSL) model of criminal behavior, researchers developed principles of effective intervention to outline the specific strategies and tools correctional practitioners can implement to reduce recidivism and improve other outcomes (e.g., Bonta & Andrews, 2017; Andrews & Dowden, 2006; Cullen & Gendreau, 2000; Gendreau, 1996). There are 15 of these principles that are organized around three themes: (1) overarching principles (i.e., respecting individuals as having agency, emphasize use of humane treatment, use a personality and cognitive social learning perspective to guide treatment, and a focus on crime prevention above punishment), (2) core principles and key clinical issues (i.e., human service, risk, need, general responsivity, specific responsivity, breadth, strength, structured assessment, and professional discretion), and (3) organizational principles (i.e., community-based, GPCSL-based staff, and risk, need, responsivity focused management) (Bonta & Andrews, 2017). These principles reflect best practices for correctional agencies in terms of approach and ideologies associated with supervision but also specific practices, including use of risk and needs assessments, individualizing services to target dynamic risk factors, treatment planning, providing rewards and sanctions at a ratio of at least 4:1, and providing an integrated approach for individuals with multiple needs (Bonta & Andrews, 2017; Gendreau et al., 1996; Smith et al., 2009).

At the core of this framework are the three principles of risk-need-responsivity (RNR), which combines an actuarial, managerial approach with a rehabilitative, clinical model of supervision (Andrews et al., 1990; Taxman & Smith, 2020). The RNR principles are designed to generate effective interventions for individuals under correctional supervision with the goal of improving treatment and reducing recidivism (Bonta & Andrews, 2017). The risk principle requires the use of a validated risk assessment to measure risk of reoffending and suggests supervision and treatment resources should be reserved for higher risk individuals. The need principle suggests interventions must target criminogenic needs or dynamic risk factors directly related to reoffending (i.e., antisocial personality, antisocial attitudes/cognitions, social supports for crime, substance abuse, inappropriate parental monitoring/discipline, school/work problems, poor self-control, lack of prosocial activities). Finally, the responsivity principle, suggests the use of social-learning and cognitive-behavioral techniques to intervene as well as the tailoring of programming to the motivation, learning styles, and strengths of the individual (Bonta & Andrews, 2017).

A meta-analysis on the effectiveness of the RNR framework suggests that full adherence to the model can reduce criminal offending by up to 32% compared to a 7% increase in recidivism in programs that did not adhere to any of the principles (Andrews et al., 1990). More recent meta-analyses confirm the benefits of adhering to the RNR model in reducing recidivism (Bonta & Andrews, 2017; Smith et al., 2009). Adherence to the RNR model in routine practice, however, is often challenging. Several research studies identify barriers to full implementation of the principles of effective intervention both at the individual and organizational level (Bonta et al., 2008; Miller & Maloney, 2013; Viglione et al., 2015; Viglione, 2017). For example, organizational commitment (Drapela & Lutze, 2009; Fixsen et al., 2009), culture (Rudes et al., 2012; Taxman et al., 2012) and staff perceptions and characteristics (Rudes, 2012; Viglione & Taxman, 2018) can complicate the change process and prevent the successful implementation of the RNR model in practice.

Community Supervision Officer Training Programs

To increase the therapeutic effectiveness of correctional interventions, scholars identified core correctional practices (CCPs) (Dowden & Andrews, 2004). The CCPs lay out a set of service delivery skills, including anticriminal modeling, effective reinforcement, effective disapproval, effective use of authority, structured learning, problem solving, cognitive restructuring, and relationship skills (Gendreau et al., 2010). Research finds use of CCPs can result in recidivism reductions at the organizational level (Farringer et al., 2019; Lowenkamp et al., 2006; Matthews et al., 2001), however, adherence and implementation of these skills in practice is difficult (Bonta et al., 2008; Dyck et al., 2018; Viglione, 2017).

Given the onus put on front-line staff (such as probation officers or POs) when implementing practices aligned with CCPs and RNR to significantly alter the way they do business on a regular basis, the effectiveness of such practices is heavily dependent on both whether and how officers use these skills (Fixsen et al., 2005; Lipsky, 1980). For example, POs often distrust risk assessment tools (Krysik & LeCroy, 2002; Schwalbe, 2004; Viglione et al., 2015; Viglione, 2017), rarely use assessed risk level to guide decisions and do not target criminogenic needs (Bonta et al., 2008), and make decisions that deviate from assessment results (Miller & Maloney, 2013). When POs do discuss criminogenic needs, they often avoid the more challenging ones that are more strongly related to recidivism (such as criminal thinking and cognitions) (Bourgon & Gutierrez, 2012), and focus heavily on employment and housing (Viglione et al., 2015). In an examination of interactions between POs and individuals on their caseload, Bonta et al. (2008) found POs implemented cognitive-behavioral techniques in only 1% of interactions, while Viglione (2017) found POs often made supervision decisions based on concerns for risk, public safety, and liability.

Research on organizational change within community supervision organizations often reports improved use of skills following training programs (Bonta et al., 2011, 2019; Labrecque et al., 2013; Latessa et al., 2013; Robinson et al., 2011, 2012; Smith et al., 2012). These studies found that POs trained specifically on the principles of RNR demonstrated better adherence to the RNR principles in practice, more frequently used cognitive-behavioral techniques, and the individuals they supervised were more likely to have better outcomes. One mechanism to promote and support change and increase adherence to the RNR model in correctional organizations is through formal training programs (Chadwick et al., 2015) that incorporate a multi-pronged approach to supporting change.

To date, there have been several attempts to integrate the principles of effective interventions into community supervision settings via specialized training programs (i.e., Staff Training Aimed at Reducing Rearrest (STARR) (Robinson et al., 2011), Effective Practices in Community Supervision (EPICS) (Latessa et al., 2013), Strategic Training Initiative in Community Supervision (STICS) (Bonta et al., 2010), and Proactive Community Supervision (PCS) (Taxman et al., 2012) for POs. While differences between these training programs exist, they all attempt to translate the RNR model into practices that increase the knowledge, understanding, and application of its principles into daily practice. Training focused on the RNR principles is critical to support skill development, as adherence to the RNR model often requires important behavioral changes in POs. These programs promote the use of the principles of effective intervention and RNR through a focus on intervention delivery during client-officer interactions (Robinson et al., 2012). Through training, POs are encouraged to develop and use cognitive-behavioral techniques to address the criminogenic needs of their probationers (Bonta et al., 2010; Latessa et al., 2013; Robinson et al., 2011, 2012).

Correctional training programs serve as a primary tool for community supervision agencies to translate the research on best practices and encourage change in the roles and behaviors of front-line staff. These models offer comprehensive skill development, providing a framework for probation and parole agencies and staff to align with changing roles and expectations as a result of scientific evidence on effective practices. Importantly, these training programs include multiple components designed to support implementation, including multi-day initial training, frequent booster trainings, assignment of peer coaches, and ongoing assessment of staff use of trained skills (Baer et al., 2007; Fixsen et al., 2005, 2009).

Overall, the research on correctional curriculums indicate positive outcomes both in terms of improved use of evidence-based supervision skills (e.g., Bonta et al., 2010; Bourgon & Gutierrez, 2012; Labrecque & Smith, 2017; Smith et al., 2012), reduced failure rates (Bonta et al., 2019; Latessa et al., 2013; Lowenkamp et al., 2014; Robinson et al., 2011, 2012) and increased client satisfaction (Alarid & Jones, 2018). Research on community supervision officer training programs found that officers who received training were more likely to discuss cognitions, peers and impulsivity with individuals they supervised compared to untrained officers (Robinson et al., 2011) and were more likely to use the principles of effective intervention post-training (Bonta et al., 2010; Bourgon & Gutierrez, 2012; Labrecque & Smith, 2017; Smith et al., 2012). These studies identified that trained officers were more likely to both discuss and address criminogenic needs, including criminal cognitions (Bonta et al., 2008, 2011; Bourgon et al., 2010; Bourgon & Gutierrez, 2012) and were more likely to use and model prosocial behavior in their interactions (Labrecque et al., 2013; Smith et al., 2012) compared to untrained officers. However, Robinson et al. (2012) found that while trained POs exhibited significantly greater use of core correctional skills, they still used those skills in fewer than 50% of their interactions with clients (Robinson et al., 2012). Outcome studies suggested probationers supervised by trained officers had reduced failure rates (Bonta et al., 2019; Latessa et al., 2013; Lowenkamp et al., 2014; Robinson et al., 2011, 2012). Evaluations of STICs found that individuals supervised by trained officers who used cognitive-behavioral techniques more frequently were more likely to experience lower the rates of reoffending (Bonta et al., 2008, 2011; Bourgon & Gutierrez, 2012).

Despite overall positive effects of community supervision officer training programs on officer skills and probationer experience and outcomes, a recent meta-analysis highlights the modest impact training has on recidivism (Chadwick et al., 2015). That is, across the ten evaluations of training programs, individuals supervised by trained officers were 13% less likely to recidivate, but the effect size was relatively small (d = 0.22). However, research also identifies the complexity of successful implementation, particularly related to encouraging wide use of newly learned skills and implementation of skills with more challenging populations (i.e., high risk). As such, one cannot expect large impacts on recidivism if PO use of skills is infrequent or lacks fidelity. While community supervision officer training programs are built on solid theory and existing evidence is promising, including a rating by Crime Solutions (crimesolutions.gov) as “promising” for STARR, EPICS, and STICS, evaluation of additional implementation efforts is critical to understand the effectiveness of the training program for specific agencies and their populations.

Implementation Challenges

Research identifies many potential barriers to successful implementation even in agencies using multi-faceted training approaches. For example, staff perceptions such as cynicism for change or a lack of readiness for change (Rudes et al., 2012) can impede change efforts. Alternatively, staff who report greater commitment to their organization are more likely to hold positive attitudes (Clegg & Dunkerly, 1980; Kerce et al., 1994; Ostroff, 1992; Wycoff & Skogan, 1994) and engage in evidence-based supervision practices (Viglione et al., 2018). Additional organizational factors related to successful EBP implementation include leadership styles (Aarons, 2006), performance-drive structure (Backer et al., 1986; Knudsen et al., 2006; Roman & Johnson, 2002), positive organizational climate (Friedmann et al., 2007; Glisson & Green, 2006; Glisson & Hemmelgarn, 1998; Henderson et al., 2007), resources and quality training (Friedmann et al., 2007; Fulton et al., 1997) and network connectedness (Fletcher et al., 2009; Friedmann et al., 2007; Henderson et al., 2007; Knudsen & Roman, 2004).

Given the vast potential barriers and facilitators associated with organizational change, agencies must engage in extensive planning and training to support efforts (Baer et al., 2007; Simpson, 2002). Research notes that training alone is not an effective implementation strategy (Azocar et al., 2003; Schectman et al., 2003; Stokes & Baer, 1977), however, it is an efficient mechanism to provide the foundation needed to support change and an opportunity to practice skills and receive feedback (Fixsen et al., 2009). The strongest implementation strategies go beyond initial training, incorporating ongoing booster training, coaching, and assessment of staff performance (Baer et al., 2007; Fixsen et al., 2005, 2009). Additionally, research on organizational change suggests that when agency include staff in the decision-making process, they are more likely to understand, accept and implement change (Steiner et al., 2011; Taxman & Belenko, 2012). The overall organizational capacity (e.g., funding, regulation, readiness for change, leadership) can play a significant role in the implementation and support of best practices (Guerrero et al., 2014; Taxman & Belenko, 2012). As part of building a capacity for change, the organization must develop and institute policies and procedures to aid in the routinization of desired actions and behaviors (Fredericksen & London, 2000). Further, formal policies and procedures can shape individual self-perceptions (March, 1994), use of discretion (Feldman, 1992), and expectations (Feldman, 1992, 2000; March, 1994). This literature suggests the importance of purposeful planning to build organizational capacity to clarify expectations and promote alignment with change.

The Current Study

While prior research suggests implementation of community supervision officer training programs and additional coaching can help improve use of skills and reduce recidivism (e.g., Bonta et al., 2010; Bourgon & Gutierrez, 2012; Labrecque & Smith, 2017; Robinson et al., 2011; Smith et al., 2012), this research still finds poor adherence and low utilization of newly trained skills (e.g., Robinson et al., 2012). One approach to address this challenge is to mandate the use of skills through formal policy. However, it is unclear whether this approach is a successful way for promoting the increased use of skills. The current study seeks to add knowledge to this inquiry by examining the impact of a policy designed to increase use of STARR skills among trained officers in one Federal probation district. As noted in previous research, even though trained officers are more likely to use skills compared to untrained officers, skill use often occurs less frequently then desired. As a means to address this common implementation challenge, the probation district in the current study developed and implemented a policy to mandate officers to use STARR skills a minimum of eight times per month. To our knowledge, there has been no previous study examining the impact of such a policy mandate on trained officer user of skills, an outcome that may have positive impacts for individuals on supervision and the community as a whole. As such, this study examined two research questions: (1) Was the policy change effective at increasing PO monthly use of STARR skills? and (2) Did the policy change have any effect on the type and rate of STARR skills used?

Method

Study Participants and Setting

This study was conducted in the Middle District of Florida (MDFL), an adult Federal Probation and Pretrial Service District. The MDFL comprises eight offices spread out across the district, which covers the central portion of Florida. Implementation of STARR in MDFL began in 2017, with a total of 51 individuals who received STARR training out of a total 96 active POs and supervisors by the time data was collected for this study. This slow roll-out of STARR was not done randomly, with officers voluntarily signing up for training or via recommendations from a supervisor.

STARR Training

Training for STARR involves an initial three-and-a-half-day classroom-style training, which focuses on introducing participants to the RNR model and discussing the theory and evidence behind STARR. An important part of this training process involves modeling of key skills by trainers and opportunities for trainees to role play and receive immediate feedback on their use of skills (Robinson et al., 2011, 2012). STARR training focuses on developing an effective relationship between the PO and probationer (Robinson et al., 2012). Officers are trained in a variety of CCPs to build a collaborative working relationship while focusing on risk reduction and accountability. The development of effective communication techniques is center to the STARR training program. As such, officers are trained in active listening, the use of open-ended questions, affirmations, reflections, summarization, and empathy. Through this collaborative relationship and use of effective communication, the goal is to collect accurate information and emphasize that the officer cares about the individuals they supervise, while supporting their own self-efficacy.

To support these goals, STARR includes ten key CCP skills including the use of role clarification to set the tone and expectations for the supervision experience, role clarification review to remind existing clients of expectations and goals, four bridging skills for POs to help probationers work toward reducing maladaptive behaviors and replacing them with pro-social ones (effective use of reinforcement, disapproval, authority, and punishment), and four intervention skills that require POs to engage in cognitive-behavioral techniques to support behavioral change and help probationers learn to avoid high-risk situations (teaching, reviewing, and applying the cognitive model and problem solving) (Table 1).¹ Officers are trained to use the skills during their interactions with probationers to lead the individual to change. While not discouraged from using STARR with low risk probationers, training encouraged officers to prioritize use with higher risk cases (including high, moderate, and low/moderate individuals assessed by PCRA 2.0, a validated risk and needs assessment).

Table 1.

Overview of 10 STARR Skills.

Skill	Explanation
Role clarification	For use at the start of a relationship to provide expectations, explain the dual role of the PO, and identify goals of the supervision process for the PO and probationer
Role clarification review	For use in an existing relationship to remind the individual of expectations, roles, and goals
Bridging skills	Used by PO to help reduce maladaptive behaviors and develop pro-social behaviors
Effective use of reinforcement	Provide reinforcement for behavior during/immediately following behavior, deliver consistently, identify behaviors from outcomes
Effective use of disapproval	Use to address problem behavior objectively, increase probationer’s investment in alternatives, address problem behavior not the person, non-confrontational
Effective use of authority	Use when problem behavior continues despite other interventions (e.g., use of disapproval), provide probationer with choices and outcome for each choice, support pro-social choice but let probationer make decision
Effective use of punishment	Use of a consequence after problem behavior identified, discuss how to avoid behavior in the future
Intervention skills	Used by PO to teach probationer skills to avoid, manage, or cope with high risk situations that may lead to criminal behavior
Teaching the cognitive model	Teaches probationer what makes a situation high risk, helps to understand the link between external events, thoughts, and behaviors, and empowers probationer by demonstrating how they can control their behavior
Applying the cognitive model	Reinforces concepts in teaching the cognitive model, provides opportunity for PO to assess probationers understanding of the cognitive model, increases probationer’s awareness of their thoughts, thinking patterns, and behaviors
Reviewing the cognitive model	Ensures probationer is practicing the cognitive model, PO can review if probationer is correctly identifying the connection between external events, thinking patterns, and behaviors, PO can reinforce use of skill
Problem solving	Use to encourage probationer to identify a problem, clarify their goals, generate alternative solutions, develop, implement, and evaluation a plan

Trained officers receive several tools to help them implement the STARR skills when they return to the office post-training. First, they receive physical skill cards that outline guidance on specific actions, strategies, and activities they can use when they interact with probationers on their caseloads. A key component of STARR, and other community supervision officer training programs, is the peer-to-peer structure of the program. To support this in practice, each newly trained individual, termed a “user,” is assigned a “coach.” These coaches are officers who have previously received STARR training, demonstrated proficiency in application of the skills, and received additional training on coaching others to use the skills. Users then audio-record interactions with probationers during which they practice STARR skills and submit these to their coach for feedback. Lastly, booster sessions are provided throughout the year to provide a mechanism for refresher training and additional opportunities for practice and problem solving (Robinson et al., 2011, 2012). In the MDFL, trained officers are required to attend one booster training every other month.

STARR Policy

Within the MDFL, an original formal, written policy detailed expectation for users and coaches. Users and coaches were required to officially log all use of STARR skills in the districts case management system. For example, when a user implemented role clarification and effective use of reinforcement with a new probationer on their caseload, they must record both of these skills on the case log for that single interaction, even if they did not audio record the interaction. The original policy did not specify a minimum number of interactions officers had to implement STARR skills in. To facilitate skill development, users and coaches were required to provide one audio-recording of a skill attempt to their assigned coach for feedback each month and must participate in a feedback session with their coach within 14 days post-submission.

The current study was part of a larger evaluation of the implementation of STARR in this probation district. About mid-way through the evaluation, MDFL leaders reviewed their data management system and identified that trained officers were not using STARR skills as frequently as they hoped.² As a result, the district passed a new policy that required all trained officers to use a minimum of eight STARR skills per month. This new minimum use requirement was communicated via an official change in the written STARR policy that went into effect starting March 1, 2019. The goal of this policy was to encourage officers to more fully integrate STARR into their supervision strategy and send the message that STARR was a priority of the agency. Given this change, the current study sought to examine officer skill use before and after implementation of this policy.

Data

Data were obtained from the districts key data management system, the Probation and Pretrial Services Automated Case Tracking System (PACTS). PACTS housed all supervision data and officer record of use of STARR skills in their personal contacts with individuals under supervision. Data obtained from PACTS included total personal contacts, total personal contacts in which STARR was used, and type of STARR skill used. Demographic data for each officer in the district including tenure, gender, and date received user/coach training was obtained from agency staff. Data was collected on the use of STARR in the 6 months prior to the policy change (September 1, 2018–February 28, 2019) and the 6 months following the policy change (March 1, 2019–August 31, 2019).

There were two types of dependent variables included in this study to capture use of STARR skills. The first was the overall reported use of STARR skills. This variable was calculated by totaling the number of STARR skills logged per officer during the “pre-policy change” period and dividing by the total number of months (6) to create the average monthly STARR skill use before the STARR use policy was implemented (pre-policy). The post-policy change variable was created by totaling the number of STARR skills logged per officer during the 6 months following the policy change and dividing by six. There was no limit on the number of STARR skills an officer could log per interaction. Individual average monthly use variables were also created for each of the 10 STARR skills following these same procedures.

The second dependent variable captured the rate of STARR skill use by trained officers. This variable was calculated by dividing the total number of personal contacts recorded by the total logged use of STARR skills pre-policy change and post-policy change. We also calculated the rate of individual STARR skills used. For these variables, we first calculated the total skill use by individual STARR skill by officer, by month. Then, we created an average for each skill pre-policy change and post-policy change. Lastly, we divided the total number of STARR contacts by the total use of STARR skills to reflect the total proportion of all interactions where officers used each individual STARR skill.

Sample Characteristics

At the time of data collection, 51 officers had been trained in STARR. Because the goal of this study was to measure change over time, the sample for the current study included only those who were active STARR users/coaches during at least one time point pre-policy change. This resulted in a final sample of 48 trained officers (Table 2). Officers were primarily female (54%), white (63%), held a graduate degree (59%), were on average 40 years old, and employed in district for an average of 13 years. Most fulfilled non-supervisory roles (75%) and on average, supervised an active caseload of 52 individuals. Of the 48 officers in the sample, 48% were trained as a coach at the time of data collection. On average, officers identified as users in the current sample were STARR trained for an average of 22 months at the time of data collection, while coaches were trained as coaches for an average of 19 months.

Table 2.

Probation Officer Characteristics (N = 48).

Characteristic	Percent (n)	M	SD	Range
Gender
Male	46% (22)	–	–	–
Female	54% (26)	–	–	–
Race
White	63% (27)	–	–	–
Nonwhite	37% (16)	–	–	–
Age	–	40	6.8	29–55
Position/rank
Probation officer	75% (36)	–	–	–
Supervisor	25% (12)	–	–	–
Caseload	–	52	14.2	30–72
Tenure	–	13	9.0	1–53
Education
Bachelor’s	41% (18)	–	–	–
Master’s	57% (25)	–	–	–
Juris Doctorate	2% (1)	–	–	–
Coach trained
Yes	48% (23)	–	–	–
No	52% (25)	–	–	–

Note. M = mean; SD = standard deviation.

Analytic Plan

Because the focus of this study was to examine the impact of the STARR use policy on skill utilization, a series of descriptive and bivariate analyses were conducted to examine change in use of skills from pre- to post-policy, including calculating mean differences with 95% confidence intervals (CIs) and conducting paired samples t-tests. Cohen’s d was also used to interpret the magnitude of the effect size following Cohen’s (1988) guidelines of 0.2 (small), 0.5 (medium), and 0.8 (large). All analyses were conducted in SPSS Version 26.

Results

Impact of Policy Change on Number of Skills Used

Given the focus of the policy change on increasing the number of STARR skills used per month, we first examined how many STARR skills trained officers reported on average for the 6 months prior to and following the policy change. On average, trained officers used 4.6 skills pre- policy change and 13.3 skills following the policy change. To measure whether there was a statistically significant mean difference between the average number of skills used in the 6 months following the policy change, we conducted a paired samples t-test. Results indicated that the increase in use of STARR skills was statistically higher post-policy change compared to pre-policy change, t (44) = 9.8, p < .001, d = 1.46. Post-policy change, the proportion of skill use increased by 8.9% (95% CI [6.8, 10.4]) compared to pre-policy change (Table 3).

Table 3.

Average Monthly Use of Skills Pre- and Post-Policy Change.

	Pre-policy change	Post-policy change
Skill	M (SD)	M (SD)	MD [95% CI]	t	d	p
All STARR use	4.6 (3.5)	13.3 (6.1)	8.6 [6.8, 10.4]	9.8	1.46	<.001
Role clarification	1.0 (0.7)	2.1 (1.1)	1.1 [0.8, 1.4]	7.5	1.10	<.001
Role clarification review	0.2 (0.6)	2.8 (3.6)	2.6 [1.6, 3.6]	5.1	0.74	<.001
Bridging skills
Reinforcement	1.1 (1.2)	3.2 (2.1)	2.1 [1.5, 2.8]	6.5	0.94	<.001
Disapproval	0.6 (0.7)	1.6 (1.2)	0.9 [0.6, 1.3]	5.9	0.86	.001
Authority	0.3 (0.4)	1.0 (0.9)	0.7 [0.5, 0.9]	6.3	0.90	<.001
Punishment	0.3 (0.3)	0.5 (0.5)	0.3 [0.1, 0.4]	3.5	0.51	<.001
Intervention skills
Teaching the CM	0.4 (0.6)	1.3 (1.5)	0.9 [0.5, 1.4]	4.4	0.63	<.001
Applying the CM	0.3 (0.6)	0.8 (0.9)	0.5 [0.2, 0.7]	4.0	0.58	<.001
Reviewing the CM	0.2 (0.6)	0.7 (1.1)	0.5 [0.3, 0.8]	4.2	0.61	<.001
Problem solving	0.1 (0.2)	0.5 (0.5)	0.5 [0.6, 1.3]	7.6	1.10	<.001

Note. M = mean; SD = standard deviation; MD = mean difference; CI = confidence interval; CM = cognitive model.

Additionally, we examined the percentage of POs who used no skills, fewer than eight skills, and eight or more skills pre- and post-policy change (Table 4). Before the STARR skill use policy went into effect, 14.6% of officers used eight or more STARR skills per month. However, after the policy was implemented, 89.6% used eight or more STARR skills, with 25% using more than double the minimum requirement.

Table 4.

Percentage of POs Meeting STARR Use Policy Expectations.

	Pre-policy change	Post-policy change
Monthly skill use	Percent (n)	Percent (n)
No skill use (0)	12.5 (6)	0.0 (0)
Below expectations (1–7)	72.9 (35)	10.4 (5)
At or above expectations (8–15)	14.6 (7)	64.6 (31)
Exceeds expectations (16–23)	0.0 (0)	16.7 (8)
Significantly exceeds expectations (>24)	0.0 (0)	8.3 (4)

Next, the data were analyzed to assess what impact the policy change had on the frequency of the types of skills officers used in STARR sessions. As illustrated in Figure 1, the most commonly used skills pre-policy change and post-policy change came from the role clarification and bridging skills categories. Pre-policy change, the most commonly used skills were effective use of reinforcement, role clarification, and effective use of disapproval. Post-policy change, the most frequently used skills remained effective use of reinforcement and role clarification.

Figure 1.

Average monthly STARR skills pre- and post-policy change by skill type.

To measure whether there was a statistically significant mean difference between the average number of specific STARR skills used in the 6 months following the policy change, we conducted a series of paired samples t-tests. Results indicated that there was a statistically significant increase in use of each of the individual STARR skills (p < .001), with the largest increases observed in the use of role clarification review (2.6% increase), effective use of reinforcement (2.1%), and role clarification (1.1%). The smallest increases were observed in use of effective use of punishment (0.3%), problem solving (0.5%), and applying (0.5%) and reviewing (0.5%) the cognitive model (Table 3). The majority of these group differences fell within the medium effect size range (d = 0.51 to 0.74), however, large effect sizes were detected for the differences in use of effective use of authority, disapproval, reinforcement, problem solving, and role clarification (d = 0.86 to 1.10).

Impact of Policy on Rate of Skills Used

Given that each officer represented in the current study did not have equal numbers of interactions with individuals on supervision, we repeated these analyses using the proportion of STARR skills used across all interactions. First, when examining the rate of STARR skills used, pre-policy change officers were most likely to use role clarification (29% of all STARR interactions), effective use of reinforcement (21%), and effective use of disapproval (16%). Following the policy-change, officers were most likely to use effective use of reinforcement (25%), role clarification review (16%), and role clarification (15%) when they elected to use a STARR skill in their interactions. Next, we examined whether there was a statistically significant mean difference between the average proportion of skill use pre-policy change to post-policy change. A paired samples t-test revealed that trained officers used STARR skills at a higher proportion in their personal contacts after the policy change (approximately 28% of contacts) compared to pre-policy change (approximately 11% of contacts), t (44) = 10.39, p < .001, d = 1.55. Post-policy change, the proportion of skill use increased by 17.2% (95% CI [13.9, 20.6]) compared to pre-policy change. Overall, officers used STARR skills 17% more frequently post-policy change (Table 5).

Table 5.

Proportion of STARR Skills Used Pre- and Post-Policy Change.

Skill	Pre-policy change	Post-policy change		T	d	p
Skill	M (SD)	M (SD)	MD [95% CI]	T	d	p
All STARR use	10.5 (8.9)	27.7 (13.1)	17.2 [13.8, 20.1]	10.4	1.55	.001
Role clarification	29.1 (19.9)	14.8 (6.9)	−14.2 [−20.3, −8.2]	−4.7	0.72	<.001
Role clarification review	3.4 (5.4)	16.1 (12.0)	12.7 [8.9, 16.4]	6.9	1.10	<.001
Bridging skills
Reinforcement	21.1 (14.0)	24.6 (12.7)	3.5 [−1.9, 8.9]	1.3	0.20	.205
Disapproval	15.6 (11.9)	11.8 (7.7)	−3.9 [−7.0, −0.5]	−2.2	0.36	.024
Authority	5.0 (6.1)	7.1 (6.5)	2.0 [−0.4, 4.5]	1.7	0.26	.100
Punishment	8.5 (9.1)	3.8 (3.5)	−4.8 [−7.5, −2.1]	−3.6	0.54	.001
Intervention skills
Teaching the CM	8.1 (7.5)	9.2 (11.9)	1.1 [−2.7, 4.8]	0.6	0.09	.571
Applying the CM	5.6 (5.6)	4.9 (4.3)	−0.7 [−2.5, 1.1]	−0.8	0.12	.424
Reviewing the CM	2.4 (6.7)	4.0 (4.3)	1.7 [0.0, 3.3]	2.1	0.32	.044
Problem solving	1.2 (3.2)	3.8 (3.6)	2.6 [1.6, 3.6]	5.4	0.84	<.001

Note. M = mean; SD = standard deviation; MD = mean difference; CI = confidence interval; CM = cognitive model.

Finally, we examined which STARR skills trained officers were more likely to implement when they decided to use a STARR skill (Table 5). Following the policy change, there were statistically significant increases in the use of three STARR skills: role clarification review (12.7% more frequent use), problem solving (2.6% more frequent use), and reviewing the cognitive model (1.7%). Statistically significant decreases were observed for three STARR skills as well: role clarification (14.2% reduction in use), effective use of punishment (4.8%), and effective use of disapproval (3.9%). While officers used effective use of reinforcement, effective use of authority, and teaching the cognitive model more frequently post-policy change (3.5%, 2%, and 1.1% respectively), these differences were not significant. Trained officers were 0.7% less likely to use applying the cognitive model after the policy change, but this difference was also not significant or substantively meaningful.

Discussion

Given the challenges associated with implementing effective correctional practices and training programs, the goal of this study was to examine whether mandating the use of trained skills via a formal policy was an effective strategy to improve implementation success. Findings from the current study revealed that trained officers increased their use of skills post-policy implementation, with a jump from an average of 4.6 skills used per month to an average of 13.5 skills used monthly. This finding is promising, given the implementation of formal policy is an inexpensive way to promote use of new and desired skillsets.

Prior to the policy mandate, trained officers used STARR skills in an average of 11% of their interactions with individuals on their caseload. While this increased post-policy change, trained officers still only used STARR in about a quarter of their interactions with clients (28%). While this is not surprising given the previous research that reports officers used trained skills less than 58% of the time (Bonta et al., 2011; Latessa et al., 2013; Robinson et al., 2011), it suggests there is still more work to be done to understand how to further encourage increased use of skills given the training program follows best practices for training (multi-day, frequent follow-ups, coaching and review of performance) (Baer et al., 2007; Fixsen et al., 2005, 2009). While the implementation of formal policy is one way to support organizational capacity for change (Fredericksen & London, 2000; Guerrero et al., 2014; Taxman & Belenko, 2012), other factors impacting capacity may need attention to further promote implementation, such as resources, readiness for change, and leadership (Guerrero et al., 2014; Taxman & Belenko, 2012). That is, this research suggests while policy can aid implementation efforts, more is needed to support routinization of new skills into practice. For example, it is possible that officers do not trust or see the value added in the use of the skills, as previous research has found in relation to the implementation of evidence-based practices in probation settings (e.g., Haas & DeTardo-Bora, 2009; Viglione, 2017; Viglione et al., 2015) or they are not comfortable using the skills in real-world settings. To encourage trained staff to use newly trained skills, they must believe it is worthwhile (Lin, 2000) and feel comfortable taking a risk to try something new (O’Reilly & Caldwell, 1985; Panzano & Roth, 2006). Research on PO decision making surrounding use of RNR principles suggests officers tended to focus on risk management, especially with riskier populations, to avoid liability while adequately controlling their caseload (Viglione, 2019). Future research should focus on exploring the decision-making processes of trained officers to understand why and under what conditions they choose or do not choose to implement trained skills specific to community supervision officer training programs.

When examining STARR skill use by type of skill, several noteworthy findings emerged. After the policy change, officers used effective use of reinforcement more frequently, while using effective use of punishment and disapproval less frequently. This potentially suggests that when officers are attempting to integrate STARR into their routine casework, they are focusing on emphasizing positives and providing reinforcement to encourage behavior change. This falls in line with research that highlights the importance of using positive reinforcement and sanctions at a 4:1 ratio (Wodahl et al., 2011) and the overall ineffectiveness of punitive strategies to reduce recidivism (MacKenzie, 2006; Petersilia & Turner, 1993). However, the lowest engagement occurred with problem solving and reviewing, applying, and teaching the cognitive model. These are often believed to be the most difficult skills taught in community supervision training programs as they require engaging in intervention techniques grounded in cognitive behavioral therapy, a clinical skill not traditionally part of probation supervision. These skills are much more time consuming and require a greater level of competency to implement as compared to role clarification and the bridging skills. However, research suggests the important role cognitive-behavioral techniques play in encouraging behavior change amongst justice populations (Wodahl et al., 2011). Previous research suggests expanding coaching sessions alone can improve the frequency and proficiency in use of skills (Labrecque & Smith, 2017), so prioritizing these intervention skills in coaching sessions and booster trainings and increasing opportunities for practice may be one way to increase officer comfort and proficiency in using these more complicated skills. And, given the general policy mandate was successful in increasing skills in the current study, perhaps mandating the frequency of use of specific types of skills would encourage officers to attempt the more difficult skills at a higher rate.

Although nearly 90% of trained officers complied with the mandated policy and used a minimum of eight skills per month, there were no stipulations in the policy that specified what happened when a trained officer did not meet these new expectations. Holding trained officers accountable in the midst of change is an ongoing problem that correctional agencies must grapple with. Agencies must develop strategies to support and encourage trained staff to use new skillsets, while holding them accountable in a way that is not punitive, but supportive of a learning and growth process. For example, officers who use trained skills below the minimum threshold could be required to attend additional training, meet with their supervisor, or have their interactions randomly observed by a coach. However, given what we know about encouraging behavior change amongst individuals on supervision, perhaps the most effective methods would focus on positive reinforcement and incentives. While financial rewards are often not feasible long-term, non-monetary incentives may prove useful (Clark & Wilson, 1961), such as provision of public praise, delegation of a STARR user of the month, permission to wear jeans on certain days for all users who meet expectations, or special parking spaces. Agency leaders should survey their staff to determine what non-monetary incentives may be most desirable (and thus, most likely to motivate them) and develop clear policies that link desired behavior with the specific rewards that can be earned.

Ultimately once all staff are trained and the agency is committed to use of skills becoming part of the culture of the agency, a formal structure to assess and monitor probation staff performance should be developed and tried to staff use of skills (Johnson et al., 2001; Taxman & Belenko, 2012). In creating this performance review criteria, front-line staff should be included to increase buy-in, ownership, and support of thew new policy (Steiner et al., 2011; Taxman & Belenko, 2012). Importantly, any formal policies developed must be clearly articulated to staff so they are aware of how expectations associated with STARR will be used to evaluate their overall job performance. Moving forward agencies can also restructure their hiring practices to both articulate expectations aligned with STARR use, but also to identify potential employees who are best suited for the job and align with the existing values and goals of the organization (Bertram et al., 2015; Vance, 2006). Interviews can include questions specifically designed to measure knowledge and experience with effective correctional practices, comfortability engaging in role-play scenarios, and ability to be coached (Bertram et al., 2015).

Limitations and Future Research

The current analyses do not account for the quality of skills used or fidelity to the STARR model, which can directly impact outcomes such as recidivism. This data is not currently available; however, the agency is committed to developing procedures to measure proficiency and quality of skill use. Because the goal of the mandated policy was not to improve quality, the focus on frequency is appropriate in the current scenario. The measure of skill use is also limited as it relies on officer self-report. While officers are required to detail their use of STARR in their case notes and supervisors can review these notes, there is no way for us to ensure officers used the skills or used them correctly in each reported instance. Future research should replicate this study with a measure of quality to understand the impact of policy mandates beyond solely self-reported frequency of use. However, theoretically, one cannot expect an increase in quality skill use until officers are routinely attempting use of skills in their everyday practice. This study provides a necessary first step in understanding the role formal policies may play in supporting change processes.

Additionally, the current study examined a relatively short follow-up period. In future studies, we plan to extend these analyses to examine longitudinal trends in skill use. Importantly, research should examine whether there is continued growth in the use of skills or a decay in skill usage. The current study also involved non-random assignment to STARR training. It is possible that the officers in the current study were intrinsically motivated to use trained skills. Once all staff receive training, future research should examine whether policy mandates have the same impact on all officers. Also, the sample size is small and represents one district in one state. Findings may not be generalizable to other districts of varying sizes and compositions. Future research should examine whether the implementation of such a policy is effective across a range of probation districts. We were also unable to examine the frequency of STARR skill use by risk level. This is an important avenue for future research to examine, as research suggests intervention should focus on higher risk clients (Bonta & Andrews, 2017). Lastly, in this study we combined coaches and users in our analyses. It is possible that use of skills may differ between these two groups as coaches receive a greater dosage of training. Future research should assess whether this mandated policy has a differential impact on coaches versus users.

Conclusion

The current study is the first known examination of the impact of a formal policy mandate on use of skills from a correctional training program. The results are promising and demonstrate that no-cost changes in organizational policy can support increased use of skills. However, more work is needed to understand the mechanisms that drive decisions to use skills to better understand how to further increase use with individuals on supervision. Research is similarly needed to understand the factors that drive increased quality in use of skills and fidelity to the training programs. Trained officers will not become proficient skill users without practice. The development of strategies to encourage and support staff while holding them accountable are key to progress.

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the United States Probation Office Middle District of Florida. We would like to thank the dedicated and hard-working staff at the Middle District of Florida for their assistance in making this research possible.

ORCID iDs

Jill Viglione

Ryan M. Labrecque

Notes

References

Aarons

G. A.

(2006). Transformational and transactional leadership: Association with attitudes towards evidence-based practice. Psychiatric Services, 57(8), 1162–1169.

Alarid

L. F.

Jones

(2018). Perceptions of offender satisfaction on probation and supervised release with STARR skill sets. Federal Probation, 82(1), 37–54.

Andrews

D. A.

Dowden

(2006). Risk principle of case classification in correctional treatment: A meta-analytic investigation. International Journal of Offender Therapy and Comparative Criminology, 50(1), 88–100.

Andrews

D. A.

Zinger

Hoge

R. D.

Bonta

Gendreau

Cullen

F. T.

(1990). Does correctional treatment work? A psychologically informed meta-analysis. Criminology, 28, 369–404.

Andrews

D. A.

Bonta

Hoge

R. D.

(1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17(1), 19–52.

Azocar

Cuffel

Goldman

McCarter

(2003). The impact of evidence-based guideline dissemination for the assessment and treatment of major depression in a managed behavioral health care organization. The Journal of Behavioral Health Services & Research, 30(1), 109–118.

Baer

J. S.

Ball

S. A.

Campbell

B. K.

Miele

G. M.

Schoener

E. P.

Tracy

(2007). Training and fidelity monitoring of behavioral interventions in multi-site addictions research. Drug and Alcohol Dependence, 87, 107–118.

Bertram

R. M.

Blase

K. A.

Fixsen

D. L.

(2015). Improving programs and outcomes: Implementation frameworks and organization change. Research on Social Work Practice, 25(4), 477–487.

Bonta

Andrews

D. A.

(2017). The psychology of criminal conduct (6th ed.). Routledge.

10.

Bonta

Bourgon

Rugge

Scott

T. L.

Yessine

A. K.

Gutierrez

L. K.

& Public Safety Canada. (2010). The strategic training initiative in community supervision: Risk-Need-Responsivity in the real world 2010-01. Public Safety Canada.

11.

Bonta

Bourgon

Rugge

Scott

Yessine

A. K.

Gutierrez

(2011). An experimental demonstration of training probation officers in evidence-based community supervision. Criminal Justice and Behavior, 38(11), 1127–1148.

12.

Bonta

Rugge

Bourgon

Wanamaker

K. A.

(2019). A conceptual replication of the strategic training initiative in community supervision (STICS). Journal of Experimental Criminology, 15(3), 397–419.

13.

Bonta

Rugge

Scott

Bourgon

Yessine

A. K.

(2008). Exploring the black box of community supervision. Journal of Offender Rehabilitation, 47(3), 248–270.

14.

Bourgon

Gutierrez

(2012). The general responsivity principle in community supervision: The importance of probation officers using cognitive intervention techniques and its influence on recidivism. Journal of Crime and Justice, 35(2), 149–166.

15.

Bourgon

Bonta

Rugge

Scott

T. L.

(2010). The role of program design, implementation, and evaluation in evidence-based real world community supervision. Federal Probation, 74, 2.

16.

Chadwick

Dewolf

Serin

(2015). Effectively training community supervision officers: A meta-analytic review of the impact on offender outcome. Criminal Justice and Behavior, 42(10), 977–989.

17.

Clark

P. B.

Wilson

J. Q.

(1961). Incentive systems: A theory of organization. Administrative Science Quarterly, 6(2), 129–166.

18.

Clegg

S. R.

Dunkerley

(1980). Organization, class and control. Routledge & Kegan Paul.

19.

Cohen

(1988). Statistical power and analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

20.

Cullen

F. T.

Gendreau

(2000). Assessing correctional rehabilitation: Policy, practice, and prospects. In Horney

(Ed.), NIJ criminal justice 2000: Volume 3—changes in decision making and discretion in the criminal justice system. National Institute of Justice.

21.

Cullen

F. T.

Gilbert

K. E.

(1982). Reaffirming rehabilitation. Anderson Publishing Company.

22.

Drapela

L. A.

Lutze

F. E.

(2009). Innovation in community corrections and probation officers’ fears of being sued: Implementing neighborhood-based supervision in Spokane, Washington. Journal of Contemporary Criminal Justice, 25, 364–383.

23.

Dowden

Andrews

D. A.

(2004). The importance of staff practice in delivering effective correctional treatment: A meta-analytic review of core correctional practice. International Journal of Offender Therapy and Comparative Criminology, 48(2), 203–214.

24.

Dyck

H. L.

Campbell

M. A.

Wershler

J. L.

(2018). Real-world use of the risk-need-responsivity model and the level of service/case management inventory with community-supervised offenders. Law and Human Behavior, 42(3), 258–268.

25.

Farringer

A. J.

Duriez

S. A.

Manchak

S. M.

Sullivan

C. C.

(2019). Adherence to “what works”: Examining trends across 14 years of correctional program assessment. Corrections: Policy, Research and Practice. Advanced online publication. https://doi.org/10.1080/23774657.2019.1659193

26.

Feldman

M. S.

(1992). Social limits to discretion (pp. 11–46). Clarendon Press.

27.

Feldman

M. S.

(2000). Organizational routines as a source of continuous change. Organization Science, 11(6), 611–629.

28.

Fixsen

D. L.

Blase

K. A.

Naoom

S. F.

Wallace

(2009). Core implementation components. Research on Social Work Practice, 19, 531–540.

29.

Fixsen

D. L.

Naoom

S. F.

Blase

K. A.

Friedman

R. M.

Wallace

(2005). Implementation research: A synthesis of the literature Tampa FL University of South Florida, Louis de la Parte Florida Mental Health Institute. The National Implementation Research Network (FMHI Publication# 231), 11, 247–266.

30.

Fletcher

B. W.

Lehman

W. E.

Wexler

H. K.

Melnick

Taxman

F. S.

Young

D. W.

(2009). Measuring collaboration and integration activities in criminal justice and substance abuse treatment agencies. Drug and Alcohol Dependence, 103, S54–S64.

31.

Fredericksen

London

(2000). Disconnect in the hollow state: The pivotal role of organizational capacity in community-based development organizations. Public Administration Review, 60(3), 230–239.

32.

Friedmann

P. D.

Taxman

F. S.

Henderson

C. E.

(2007). Evidence-based treatment practices for drug-involved adults in the criminal justice system. Journal of Substance Abuse Treatment, 32(3), 267–277.

33.

Fulton

Latessa

E. J.

Stichman

Travis

(1997). The state of ISP: Research and policy implications. Federal Probation, 61(4), 65–75.

34.

Gendreau

(1996). The principles of effective intervention with offenders. In Harland

(Ed.), Choosing correctional options that work: Defining the demand and evaluating the supply. Sage.

35.

Gendreau

Andrews

D. A.

Theriault

(2010). Correctional Program Assessment Inventory—2010 (CPAI-2100). University of New Brunswick.

36.

Gendreau

Little

Goggin

(1996). A meta-analysis of the predictors of adult offender recidivism: What works! Criminology, 34(4), 575–608.

37.

Gleicher

Manchak

S. M.

Cullen

F. T.

(2013). Creating a supervision toolkit: How to improve probation and parole. Federal Probation, 77, 22–27.

38.

Glisson

Green

(2006). The effects of organizational culture and climate on the access to mental health care in child welfare and juvenile justice systems. Administration and Policy in Mental Health and Mental Health Services Research, 33(4), 433–448.

39.

Glisson

Hemmelgarn

(1998). The effects of organizational climate and interorganizational coordination on the quality and outcomes of children’s service systems. Child Abuse & Neglect, 22, 401–421.

40.

Guerrero

E. G.

Aarons

G. A.

Palinkas

L. A.

(2014). Organizational capacity for service integration in community-based addiction health services. American Journal of Public Health, 104(4), e40–e47.

41.

Haas

S. M.

DeTardo-Bora

K. A.

(2009). Inmate reentry and the utility of the LSI-R in case planning. Corrections Compendium, 34(1), 11–16.

42.

Henderson

C. E.

Young

D. W.

Jainchill

Hawke

Farkas

Davis

R. M.

(2007). Adoption of evidence-based drug abuse treatment practices for juvenile offenders. Journal of Substance Abuse Treatment, 32(3), 279–290.

43.

Johnson

Cassell

Duberley

(2001). Performance evaluation and control: Supporting organizational change. Management Decision, 39(10), 841–851.

44.

Kerce

E. W.

Magnusson

Rudolph

(1994). The attitudes of Navy corrections staff members: What they think about confines and their jobs. Navy Personnel Research and Development Center.

45.

Klockars

(1972). A theory of probation supervision. Journal of Criminal Law, Criminology, and Police Science, 64(4), 549–557.

46.

Knudsen

H. K.

Roman

P. M.

(2004). Modeling the use of innovations in private treatment organizations: The role of absorptive capacity. Journal of Substance Abuse Treatment, 26(1), 51–59.

47.

Knudsen

H. K.

Ducharme

L. J.

Roman

P. M.

(2006). Early adoption of buprenorphine in substance abuse treatment centers: Data from the private and public sectors. Journal of Substance Abuse Treatment, 30(4), 363–373.

48.

Krysik

LeCroy

C. W.

(2002). The empirical validation of an instrument to predict risk of recidivism among juvenile offenders. Research on Social Work Practice, 12(1), 71–81.

49.

Labrecque

R. M.

Smith

(2017). Does training and coaching matter? An 18-month evaluation of a community supervision model. Victims and Offenders, 12(2), 233–252.

50.

Labrecque

R. M.

Smith

Schweitzer

Thompson

(2013). Targeting antisocial attitudes in community supervision using the EPICS model: An examination of change scores on the Criminal Sentiment Scale. Federal Probation, 77(3), 15–20.

51.

Latessa

E. J.

Johnson

S. L.

Koetzle

(2020). What works (and doesn’t) in reducing recidivism. Routledge.

52.

Latessa

E. J.

Smith

Schweitzer

Labrecque

R. M.

(2013). Evaluation of the effective practices in community supervision model (EPICS) in Ohio. Office of Criminal Justice Services in Columbus.

53.

Lin

A. C.

(2000). Reform in the making: The implementation of social policy in prison. Princeton University Press.

54.

Lipsey

M. W.

Cullen

F. T.

(2007). The effectiveness of correctional rehabilitation: A review of systematic reviews. Annual Review of Law and Social Science, 3, 297–320.

55.

Lipsky

(1980). Street-level bureaucracy: Dilemmas of the individual in public services. Russell Sage Foundation.

56.

Lowenkamp

C. T.

Holsinger

Robinson

C. R.

Alexander

(2014). Diminishing or durable treatment effects of STARR? A research note on 24-month re-arrest rates. Journal of Crime and Justice, 37(2), 275–283.

57.

Lowenkamp

C. T.

Latessa

E. J.

Smith

(2006). Does correctional program quality really matter? The impact of adhering to the principles of effective intervention. Criminology and Public Policy, 5(3), 575–594.

58.

MacKenzie

D. L.

(2006). What works in corrections: Reducing the criminal activities of offenders and delinquents. Cambridge University Press.

59.

March

J. G.

(1994). Primer on decision making: How decisions happen. Simon and Schuster.

60.

Matthews

Hubbard

D. J.

Latessa

E. J.

(2001). Making the next step: Using evaluability assessment to improve correctional programming. The Prison Journal, 18(4), 454–472.

61.

Miller

Maloney

(2013). Practitioner compliance with risk/needs assessment tools: A theoretical and empirical assessment. Criminal Justice and Behavior, 40(7), 716–736.

62.

Nagin

D. S.

Cullen

F. T.

Jonson

C. L.

(2009). Imprisonment and reoffending. Crime & Justice, 38, 115–413.

63.

O’Reilly

C. A.

Caldwell

D. F.

(1985). The impact of normative social influence and cohesiveness on task perceptions and attitudes: A social information processing approach. Journal of Occupational Psychology, 58(3), 193–206.

64.

Ostroff

(1992). The relationship between satisfaction, attitudes, and performance: An organizational level analysis. Journal of Applied Psychology, 77, 963–974.

65.

Panzano

P. C.

Roth

(2006). The Decision to adopt evidence-based and other innovative mental health practices: Risky business? Psychiatric Services, 57(8), 1153.

66.

Paparozzi

M. A.

Gendreau

(2005). An intensive supervision program that worked: Service delivery, professional orientation, and organizational supportiveness. The Prison Journal, 85(4), 445–466.

67.

Petersilia

Turner

(1993). Intensive probation and parole. Crime and Justice, 17, 281–335.

68.

Robinson

C. R.

VanBenschoten

Alexander

Lowenkamp

C. T.

(2011). A random (almost) study of Staff Training Aimed at Reducing Re-Arrest (STARR): Reducing recidivism through intentional design. Federal Probation, 75, 57.

69.

Robinson

C. R.

Lowenkamp

C. T.

Holsinger

A. M.

VanBenschoten

Alexander

Oleson

J. C.

(2012). A random study of Staff Training Aimed at Reducing Re-arrest (STARR): Using core correctional practices in probation interactions. Journal of Crime and Justice, 35(2), 167–188.

70.

Roman

P. M.

Johnson

J. A.

(2002). Adoption and implementation of new technologies in substance abuse treatment. Journal of Substance Abuse Treatment, 22(4), 211–218.

71.

Rudes

D. S.

(2012). Getting technical: Parole officers’ continued use of technical violations under California’s parole reform agenda. Journal of Crime and Justice, 35(2), 249–268.

72.

Rudes

D. S.

Lerch

Taxman

F. S.

(2012). Implementing a reentry framework at a correctional facility: Challenges to the culture. Journal of Offender Rehabilitation, 50(8), 467–491.

73.

Rudes

Viglione

Taxman

F. S.

(2013). Professional ideologies in United States probation and parole. In Durnescu

McNeill

(Eds.), Understanding penal practice (pp. 11–29). Routledge.

74.

Schectman

J. M.

Schroth

W. S.

Verme

Voss

J. D.

(2003). Randomized controlled trial of education and feedback for implementation of guidelines for acute low back pain. Journal of General Internal Medicine, 18(10), 773–780.

75.

Schwalbe

(2004). Re-visioning risk assessment for human service decision making. Children and Youth Services Review, 26(6), 561–576.

76.

Sherman

L. W.

Gottfredson

MacKenzie

D. L.

Eck

Reuter

Bushway

(1998). Preventing crime: What works, what doesn’t, what’s promising. National Institute of Justice.

77.

Simpson

D. D.

(2002). A conceptual framework for transferring research to practice. Journal of Substance Abuse Treatment, 22(4), 171–182.

78.

Skeem

J. L.

Manchak

(2008). Back to the future: From Klockars’ model of effective supervision to evidence-based practice in probation. Journal of Offender Rehabilitation, 47(3), 220–247.

79.

Smith

Gendreau

Swartz

(2009). Validating the principles of effective intervention: A systematic review of the contributions of meta-analysis in the field of corrections. Victims and Offenders, 4(2), 148–169.

80.

Smith

Schweitzer

Labrecque

R. M.

Latessa

E. J.

(2012). Improving probation officers’ supervision skills: An evaluation of the EPICS model. Journal of Crime and Justice, 35(2), 189–199.

81.

Steiner

Travis

L. F.

Makarios

M. D.

(2011). Understanding parole officers’ responses to sanctioning reform. Crime & Delinquency, 57(2), 222–246.

82.

Stokes

T. F.

Baer

D. M.

(1977). An implicit technology of generalization. Journal of Applied Behavior Analysis, 10(2), 349–367.

83.

Taxman

F. S.

(2002). Supervision – Exploring the dimensions of effectiveness. Federal Probation, 66(2), 14–27.

84.

Taxman

F. S.

Belenko

(2012). Implementing evidence-based practices in community corrections and addiction treatment. Springer.

85.

Taxman

F. S.

Henderson

Young

D. W.

Farrell

(2012). The impact of training interventions on organizational readiness to support innovations in juvenile justice offices. Administration of Mental Health Policy and Mental Health Services Research, 41(2), 1–12.

86.

Taxman

F. S.

Smith

(2020). Risk-need-responsivity (RNR) classification models: Still evolving. Aggression and Violent Behavior, 2, 101459.

87.

Vance

R. J.

(2006). Employee engagement and commitment: A guide to understanding, measuring and increasing engagement in your organization. SHRM Foundation.

88.

Viglione

(2017). Street-level decision making: Acceptability, feasibility, and use of evidence-based practices in adult probation. Criminal Justice and Behavior, 44(10), 1356–1381.

89.

Viglione

(2019). The risk-need-responsivity model: How do probation officers implement the principles of effective intervention? Criminal Justice and Behavior, 46(5), 655–673.

90.

Viglione

Alward

L. M.

Sheppard

D. L.

(in press). Staff Training Aimed at Reducing Rearrest: Probation officer attitudes and experiences. European Journal of Probation.

91.

Viglione

Blasko

B. L.

Taxman

F. S.

(2018). Organizational factors and probation officer use of evidence-based practices: A multilevel examination. International Journal of Offender Therapy and Comparative Criminology, 62(6), 1648–1667.

92.

Viglione

Rudes

D. S.

Taxman

F. S.

(2015). Misalignment in supervision: Implementing risk/needs assessment instruments in probation. Criminal Justice and Behavior, 42(3), 263–285.

93.

Viglione

Taxman

F. S.

(2018). Low risk offenders under probation supervision: Risk management and the risk-needs-responsivity framework. Criminal Justice and Behavior, 45(12), 1809–1831.

94.

Wodahl

E. J.

Garland

Culhane

S. E.

McCarty

W. P.

(2011). Utilizing behavioral interventions to improve supervision outcomes in community-based corrections. Criminal Justice and Behavior, 38(4), 386–405.

95.

Wycoff

M. A.

Skogan

W. G.

(1994). The effect of a community policing management style on officers’ attitudes. Crime & Delinquency, 40(3), 371–383.