Probation Staff Attitudes Toward Substance Abuse Treatment and Evidence-Based Practices

Abstract

Given the substantial need for and relatively low access to effective substance use disorder treatment for people on probation, it is critical to understand organizational and staff attitudes that may hinder or facilitate treatment linkage and willingness to adopt evidence-based practices. This study used survey data from a large county probation department to assess staff members’ attitudes and perceptions regarding their organization’s climate for innovation, role of substance use disorder treatment, support for evidence-based treatment, and organizational barriers to change. Probation staff were open to incorporating treatment into probation supervision, expressed support for rehabilitation models, and agreed that they would adopt innovations if required or they found them to be appealing. However, they expressed some concerns about the level of agency support for innovation and collaboration. Attitudes and perceptions varied by staff characteristics. Implications for expanding organizational change and adoption of evidence-based treatment practices in probation are discussed.

Keywords

community corrections probation staff drug treatment staff attitudes evidence-based practices

More than 3.9 million U.S. adults (one-in-60) were serving a probation sentence at the end of 2013, far more than the 2.2 million who were incarcerated (Glaze & Kaeble, 2014). Given the substantial proportion of offenders with histories of substance use disorders (SUD; Belenko & Peugh, 2005; Mumola & Karberg, 2006; Taxman, Perdoni, & Harrison, 2007a), there is a high burden on the probation system to find effective and efficient processes to ameliorate its effects on probation outcomes. Probation agencies need to consider how best to identify probationers with SUDs, refer probationers to appropriate treatment, and monitor progress in treatment. However, expanding probation agencies’ focus on SUDs is likely to require shifts in organizational missions and processes, involving both supervisors and line staff, which can be quite difficult to achieve (Taxman & Belenko, 2012). The present case study seeks to add to the limited literature on probation staff readiness for organizational change by examining staff perceptions of organizational culture and their attitudes toward SUD treatment and evidence-based practices (EBPs) in a large, urban probation department. We also examine whether these key constructs differed by staff characteristics.

SUDs and Treatment Needs in Probation Populations

The most recent national survey of probationers, conducted in 1995, found that 24% of probationers had a history of alcohol abuse or dependence (using Diagnostic and Statistical Manual of Mental Disorders [4th ed.; DSM-IV]; American Psychiatric Association, 1994), and 69% had a history of illicit drug use (Mumola & Bonczar, 1998). Unfortunately, there have not been any more recent systematic nationally representative studies of SUD prevalence or treatment needs among probationers since that 1995 survey (Lurigio, Cho, Swartz, Graf, & Pickup, 2003). However, recent national data from the National Household Survey on Drug Use and Health (NSDUH) found that people under probation supervision have four times the need for treatment as the general population (Substance Abuse and Mental Health Services Administration [SAMHSA], 2014b). Further, among male probationers aged 18 to 49 (the largest segment of the probation population), 40.3% had an alcohol or drug disorder in 2012; this prevalence was relatively stable over the previous decade (SAMHSA, 2014a)

More specific studies suggest that substance use prevalence is much higher among probationers than in the general population, although rates vary across jurisdictions (perhaps due in part to different measures). Blevins and Morton (1996) found that 49% of probationers in a local sample had Michigan Alcohol Screening Test scores indicating problem drinking. Among felony and misdemeanor probationers in three Texas counties, 21% had problems with alcohol and/or drug abuse and 38% had problems with alcohol and/or drug dependence (based on DSM-IV criteria; Maxwell & Wallisch, 1998). Piazza and Yeager (1993) found that 66.7% of probationers in one state either self-identified as or met criteria for being drug dependent, and 14.3% were classified as abusers. Using the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM-III-R; American Psychiatric Association, 1987) to estimate drug use, alcohol use, and treatment needs, Lurigio et al. (2003) found that 43% of adult probationers in Illinois needed alcohol or drug treatment. Evidence of SUDs among other offender populations—60% to 70% among all justice-involved individuals (Caudy, Tang, Wooditch, & Taxman, 2014) and 70% of prison and jail inmates (Belenko & Peugh, 2005)—also suggest a high rate of SUDs for probationers.

Substance use among probation populations is a concern because probationers with a SUD history were 160% more likely than others to have their probation revoked, 64% more likely to be re-arrested, and 130% more likely to receive a technical violation (Olson & Lurigio, 2000). In addition to the implications of these findings for individual probationers, failure on probation places further strains on criminal justice system costs, by leading to more costly incarceration to supervise those individuals.

Probation Agency Responses to Treatment Needs

In theory, probation agencies are in a unique position to address SUDs, given the widespread use of risk assessment tools and case supervision plans. Yet, results from a 2005 national probability sample of community corrections agencies (National Criminal Justice Treatment Practices Survey) found that community corrections agencies provide daily treatment slots for less than 10% of substance-involved individuals under their supervision (Taxman et al., 2007a), and only about half of the agencies routinely use standardized SUD assessment tools (Taxman, Cropsey, Young, & Wexler, 2007b). In addition to the low utilization of treatment, the available treatment was neither intensive nor evidence-based; the most common intervention was drug education (Taxman et al., 2007a). SUD counseling of up to 4 hours per week was provided in less than half of jurisdictions, and 21.2% offered 5 to 25 hours of treatment per week. Consistent with the indications of low treatment utilization, the 1995 national survey found that only 17% of adult probationers were receiving drug treatment while on probation (Mumola & Bonczar, 1998).¹ In addition, data from the 2012 National Household Survey on Drug Use and Health (NSDUH) on male probationers aged 18 to 49 indicate that although 45.9% needed SUD treatment, only 23.8% had received treatment in the past year, and 9.5% were currently in treatment (SAMHSA, 2014a).

This treatment gap for probationers (Belenko, Hiller, & Hamilton, 2013; Taxman, Perdoni, & Caudy, 2013) is noteworthy given that several studies have identified benefits of probation-based drug treatment. California’s Proposition 36 (Substance Abuse and Crime Prevention Act) was enacted in 2001 to reduce jail and prison crowding by diverting all non-violent drug offenders to community-based probation supervision and treatment (Evans & Longshore, 2004). Overall, it resulted in significant decreases in drug use and criminality from baseline to 12-month follow-up (Evans, Jaffe, Urada, & Anglin, 2012). Krebs, Strom, Koetse, and Lattimore (2009) found that including drug treatment along with Intensive Probation Supervision programs was associated with a 10% to 20% reduction in recidivism. Finally, a recent meta-analysis of supervision reaffirmed the importance of treatment: Probation supervision that included treatment reduced recidivism by 10%, and probation supervision that used the risk-need-responsivity (RNR) framework reduced recidivism rates by 16% (Drake, 2011).

Implementing Change in Probation Agencies

Over the past two decades, the National Institute of Corrections (NIC) has encouraged community corrections agencies to adopt and implement EBPs, including SUD treatment (Crime and Justice Institute, 2009). According to the NIC, expanding use of EBPs requires the staff to embrace new ideas and practices as well as increased staff support for organizational change and interagency collaboration (Center for Effective Public Policy, 2010; Taxman & Belenko, 2012; Taxman, Henderson, & Belenko, 2009).

However, considerable gaps persist in the integration of effective drug treatment into the probation process (Taxman & Belenko, 2012; Taxman & Thanner, 2006; Taxman et al., 2007a) and blending public safety and public health approaches to crime control. A national survey of 12 probation and parole agencies found that only one provided access to evidence-based medication-assisted treatment for opioid-dependent offenders (MAT), yet there was support from most of these agencies to expand the use of MAT (Friedmann et al., 2012).

The lack of alignment between the criminal justice and public health missions systems is one major barrier to a more seamless and integrated process for assessing, referring, and treating probationers for SUDs (Taxman, 1999; Taxman & Belenko, 2012). Additional knowledge is needed about probation staff attitudes toward treatment and organizational innovation to better develop and test new models for improving probation innovations that increase access to effective treatment. Such interventions will need to be implemented at the organizational and staff levels to produce sustainable organizational change (Aarons, Hurlburt, & Horwitz, 2011; Proctor et al., 2009) that allows the probation officer to take increased ownership of the offender change process and be willing to implement changes (Drapela & Lutze, 2009; Taxman, 2009; Taxman & Belenko, 2012).

With few prior studies on implementation challenges and practices in probation agencies, the literature on parole agencies offers some insights into the role of officer characteristics and beliefs related to organizational change. Findings include staff cynicism, a lack of readiness to change (Rudes, Lerch, & Taxman, 2011), and perceived lack of resources to support change (Rudes et al., 2011; Schlager, 2008). Despite these potential implementation barriers, there is evidence that attitudes toward innovation can be shaped by staff relationships and how the change is implemented (Steiner, Travis, & Makarios, 2011) as well as policy initiatives to support organizational change (Young, Farrell, Henderson, & Taxman, 2009). For example, Schlager (2008) found that parole staff agree with the importance of providing treatment for offenders. Whether these views are common within probation agencies is largely unknown.

Schlager (2008) found that parole staff believed that there were not enough staff to implement changes in treatment programming, and that training was of low quality and could not generate high-quality programming. Rudes et al. (2011) found that staff viewed their agency as not having sufficient funding and staffing, reported cynicism about the agency’s ability to undergo change, and perceived low levels of support for case planning from managerial level staff. Staff felt that supervisors were unfocused on specific performance outcomes and were unaware of the agency’s future direction. Staff also perceived the organization to be closed to innovation and new ideas and themselves as unwilling to take risks in their job.

Attitudes also differ between parole line staff and administrators. Parole line staff were slightly less likely than administrators to have favorable perceptions of treatment and of crime reduction strategies, and were slightly more likely to have favorable views of sanction heavy supervision strategies (Schlager, 2008). Steiner et al. (2011) assessed how officer factors affected perceptions of various aspects of a parole department’s new supervision strategy, finding that satisfaction with one’s supervisor was inversely associated with perceptions of the strategy’s effectiveness. Years of service and caseload size were inversely related to officers’ understandings of the purpose of the strategy. Satisfaction with one’s supervisor, perceptions of other purposes of the strategy, and perceptions of changes with case management were inversely related to perceptions of offender behavior being easier to manage. Finally, parole officers’ beliefs that their views and skills were considered in the design of new department strategies were associated with a greater understanding of the strategy’s purpose, increased perceptions of the strategy’s effectiveness, and perceptions that offender behavior was easier to manage.

The extant literature suggests the importance of addressing system, organizational, and staff barriers for improving the integration of treatment in the probation process. However, it is difficult to design such initiatives given the limited prior research on the attitudes of probation staff toward innovation and treatment. The goal of the current case study is thus to provide initial data on the perceptions and attitudes of probation staff in several key areas that potentially could affect a probation agency’s ability to improve SUD treatment: organizational climate for innovation, adoption of EBPs, the role of treatment in probation supervision, and organizational barriers for change. A key research question is whether these key constructs differed by staff characteristics. For example, the type of degree is expected to influence attitudes because of the potentially different exposure to substance use problems and treatment possibilities due to these differing life experiences. Research on treatment staff has found that counselors with advanced degrees are more open to innovation and EBPs (Knudsen, Ducharme, Roman, & Link, 2005). In a similar vein, officers supervising drug caseloads might be more supportive of rehabilitation.

Methods

Study Setting

The study took place in a large urban county probation department in the Northeast United States that supervises about 45,000 offenders per year. Probation officers supervising general caseloads may recommend that individual probationers receive an assessment or referral to treatment by a central behavioral health agency. This can occur as a result of a positive drug test, other indication that the probationer is using drugs, or self-disclosure by the probationer that he or she is using illegal drugs or has a drug problem. Two specialized programs have more formal procedures and standards for assessing, referring to treatment, and monitoring those in treatment. One program was established to reduce jail overcrowding by allowing low-risk offenders early release into drug or alcohol treatment, and they are supervised by a special unit of officers. At the time of the present study, there were approximately 1,100 probationers in the program, with approximately 100 probationers per officer. A second special initiative diverts higher-risk offenders from state and county prisons into drug treatment. This program typically begins with home confinement, followed by residential treatment, then outpatient treatment. There were 10 officers and 570 probationers in this program at the time of this study.

Survey Procedures and Sample

During February and March 2013, a web-based survey was used to assess probation staff experiences, attitudes, and perceptions of SUDs and treatment services for probationers, as well as attitudes and beliefs regarding EBPs and the agency’s culture. To inform the survey design and content, key informant interviews were first conducted with probation and treatment staff knowledgeable about the treatment assessment, referral, and monitoring processes. The web-based survey included questions about demographic information as well as psychometrically sound and previously validated scales (discussed below). All probation officers and administrators likely to be involved in supervising probationers in SUD treatment were targeted for participation (N = 155). The target population (N = 155) was contacted via work emails with a brief explanation of and internet link to the survey. This email was accompanied by a letter of support from the department chief. Each survey was anonymous, linked to a research ID, and took about 30 min to complete. As an incentive for participation, respondents could print out a survey completion sheet at the end of the survey and receive one hour of training credit toward their annual requirement of 40 hours.

Among the 155 staff targeted, 105 participated for a response rate of 67.7%. Due to missing item response, the total N varies for each item and scale. Table 1 summarizes the respondent characteristics. Participants ranged between 23 and 68 years of age, with a mean of 36.1 years; 55.2% of the sample was male. Most respondents were non-Hispanic (94.2%), with 59.8% White and 34.3% African American. About one quarter had earned a master’s degree (23.8%), and 75.2% had a bachelor’s degree. Criminal justice was the most common area of the respondents’ highest degree (66.0%). Most respondents were line probation officers (85.7%), and the remainder supervisors. The number of years employed in the field ranged between a few months and 25 years, with a mean of 8.7 years. Respondents’ caseloads included general supervision (44.2%), specialized drug caseloads (15.4%), anti-violence (i.e., high-risk probationers, 27.9%), and other (2.9%).

Table 1.

Staff Respondent Characteristics.

	n	%^a
Age (M = 36.1, SD = 9.2, range = 23-68)	102	—
Gender	105	—
Male	58	55.2
Female	47	44.8
Ethnicity	104	—
Not Hispanic	98	94.2
Hispanic	6	5.8
Race	102	—
White	61	59.8
African American	35	34.3
American Indian or Alaska Native	3	2.9
Asian	2	2.0
Unknown	1	1.0
Highest level of education	105	—
Bachelor’s degree	79	75.2
Master’s degree	25	23.8
High school or equivalent	1	1.0
Area of highest degree	103^b	—
Criminal justice	68	66.0
Psychology or mental health counseling	11	10.7
Other	9	8.3
Social work	5	4.9
Education/counseling education/education psychology	4	3.9
Community/human services (including law, corrections)	4	3.9
Sociology	3	2.8
Law enforcement	2	1.9
Political science	2	1.9
Formal job title	105	—
Probation officer	90	85.7
Probation officer supervisor	12	11.4
Other	2	1.9
Division director	1	1.0
Years employed in current field (M = 8.7, SD = 5.8, range = 0.3-25)	101	—
Type of caseload	104	—
General supervision	46	44.2
Anti-violence	29	27.9
Specialized drug caseloads	16	15.4
No caseload	10	9.6
Other	3	2.9

Percent values are calculated using the valid N for each variable, and not the overall N of 111.

Although 103 participants indicated their area of highest degree, the category totals add to 108 because five of the participants had degrees in two fields.

Scales and Individual Survey Items

The survey used Likert scales (strongly disagree = 0 to strongly agree = 5) for items from the Evidence-Based Practices Attitudes Scale (EBPAS; Aarons, 2004; Aarons, McDonald, Sheehan, & Walrath-Greene, 2007), the Medications Opinions Survey (MOS; Rieckmann, Daley, Fuller, Thomas, & McCarty, 2007), the National Criminal Justice Treatment Practices Survey (NCJTPS; Taxman, Young, & Fletcher, 2007c), and the Organization Readiness to Change instrument (ORC; Lehman, Greener, & Simpson, 2002). Principal Component Analysis (PCA) was used to derive subscales, using SPSS v.21. Missing values were replaced with the mean of each item. The Kaiser–Meyer–Olkin (KMO) measure ensured that the sample size was sufficient to produce reliable PCA results, while Bartlett’s test of sphericity was used to ensure adequate correlations between the variables to be reduced to a smaller number of components. The KMOs were greater than .6 and the Bartlett’s tests were all significant at p < .001 for scales that produced more than one component, indicating proper sample size and adequate correlations. All scales had at least two correlations of at least .30, and all determinants were greater than 0. Variables with communalities (the amount of variance explained by each factor for the variables) that explained less than 50% of the variance were removed from the model and the PCA was re-run.² Residuals were computed between observed and reproduced correlations. Fewer large residuals (those greater than .05) are desired, and most of the scales had a high percentage of residuals with absolute values greater than .05. Items that cross-loaded between components were placed with the subscale in which they made the most theoretical sense.

Certain sets of survey items either did not produce clear factors or were not conceptually appropriate to be divided into subscales. These included a set of questions from the NCJTPS regarding opinions on SUD treatment programs and the ORC items. These items (see Appendix A) were therefore assessed as independent individual items.

Ordinary least squares regression models assessed whether age, race, gender, number of years employed, type of caseload supervised, being a line officer, level of education, and major area of study were related to mean scores on 10 attitude and belief scales (explained below).

Results

Attitudinal Scales

Analysis of the EBPAS items found three subscales with α > .70 that closely matched the four reported by Aarons (2004): openness toward new interventions (α = .74), likelihood to adopt intervention if required to do so (α = .97), and likelihood to adopt if the intervention was appealing on different levels (α = .82). Analysis of a set of NCJTPS items regarding the mission/goals of corrections found four factors: support for punishment/deterrence-based responses to crime (α = .90), support for rehabilitative-based responses to crime (α = .91), belief in work environment being a priority of the agency (α = .80), and belief in offering direct services or activities being a priority of the agency (α = .85). Analysis of NCJTPS items related to organization culture and climate yielded a single factor that captured agreement that the agency is innovative, collaborative, and supportive (α = .92). Items adopted from the MOS assessed attitudes toward evidence-based treatment practices. Although there were no previously validated scales, analysis revealed a factor capturing positive attitudes toward EBPs (α = .83). The individual items included in each scale are presented in Appendix B.

Attitudes Toward Treatment and EBPs

Using the above scales, we found that overall, probation staff is open to innovations (mean 3.9). There was strong agreement with the likelihood of adopting interventions if required to do so by management (mean 4.2) and agreement with the likelihood of adopting interventions if the intervention were appealing on different levels (mean 3.9). There was weak agreement with divergent feelings toward EBPs (mean 2.8), but only moderate agreement with the factor capturing positive attitudes toward EBPs (mean 3.4). There was only moderate support for punishment/deterrence-based responses to crime (mean 3.0), but very high support for rehabilitative-based responses (mean 4.2). There was weak agreement in the work environment being a priority of the agency (mean 2.7) but higher agreement regarding the importance of offering of direct services or activities being a priority of the agency (mean 3.7). Finally, there was only moderate agreement that the organization culture and climate is innovative, collaborative, and supportive (mean 3.0). These results are summarized in Table 2.

Table 2.

Derived Scales of Staff Attitudes.

	α	M	SD
Openness	0.74	3.9	0.5
Divergence	0.55	2.8	0.6
Requirements	0.97	4.2	1.0
Appeal	0.82	3.9	0.7
Attitudes toward EBPs	0.83	3.4	0.6
Punishment/deterrence	0.90	3.0	0.8
Rehabilitation	0.91	4.2	0.7
Work environment as priority	0.80	2.7	1.0
Direct services or activities as priority	0.85	3.7	0.8
Organization culture and climate	0.92	3.0	0.8

Note. N = 95 (Ns are lower for some items because of missing data). EBP = evidence-based practices.

Subgroup Comparisons

Regression models were estimated to examine how individual staff characteristics predicted five of the derived constructs: openness toward new interventions, likelihood to adopt new interventions if required to do so, likelihood to adopt new interventions if the intervention was appealing on different levels, support for punishment/deterrence responses to crime, and support for rehabilitative responses to crime. The results (Table 3) indicated that female staff expressed more openness toward new interventions than did males (β = .237, p < .05) and officers employed for a greater number of years indicated a lower likelihood of adopting new interventions if it was appealing on different levels (β = −.329, p < .05). The type of officer approached significance in predicting support for rehabilitative responses: Officers supervising specialized caseloads were more supportive than those supervising non-specialized caseloads (β = .230, p < .10).

Table 3.

Regression Model Results: Relationships Between Support for Punishment/Deterrence and Rehabilitative Responses to Crime and Attitudes Toward Innovation and EBPs.

	Openness	Requirements	Appeal	Attitudes toward EBPs
	β (SE)	β (SE)	β (SE)	β (SE)
Punishment/deterrence responses	−.007 (.064)	−.202 (.152)+	−.173 (.096)	.173 (.080)
Rehabilitative responses	.401 (.079)***	.157 (.192)	.290 (.119)*	.348 (.109)**
Age	−.052 (.008)	−.002 (.018)	.065 (.012)	.215 (.009)
Gender	.118 (.104)	.132 (.247)	.017 (.156)	−.078 (.129)
Race	.092 (.102)	.004 (.244)	−.009 (.155)	−.142 (.127)
Officer type	.106 (.160)	−.024 (.382)	−.127 (.240)	.083 (.209)
Caseload type	−.018 (.138)	.169 (.329)	.084 (.208)	.144 (.173)
Number of years employed	.040 (.012)	−.124 (.029)	−.330 (.018)	−.186 (.015)
Highest degree	−.163 (.112)	.030 (.268)	−.151 (.169)	−.035 (.141)
Type of degree	.093 (.109)	−.019 (.259)	.013 (.165)	.020 (.135)

Note. EBP = evidence-based practices.

p < .10. *p < .05. **p < .01. ***p < .001.

Regression models were estimated that included values such as support for punishment/deterrence and rehabilitative procedures with demographic characteristics to predict openness, requirements, appeal, and attitudes toward EBPs. Greater support for rehabilitative responses was significantly related to more openness toward new interventions (β = .401, p < .001), a greater likelihood of adopting new interventions if they were appealing (β = .290, p < .05), and more positive attitudes toward EBPs (β = .348, p < .05). Greater support for punishment/deterrence responses to crime was marginally related to decreases in the likelihood of adopting new interventions if required to do so (β = −.202, p < .10).

Opinions on Guidance and Training Needs

The ORC items captured respondents’ agreement or disagreement with a series of statements regarding their agency’s guidance needs as well as individual training needs (Table 4). It is notable that about two thirds of probation staff agreed or strongly agreed that their agency needs additional guidance in matching needs with services, and that their agency needs additional guidance in developing more effective therapies and using client assessments to document program effectiveness. Further, about half of the staff agreed that they individually need more training in assessing client problems and needs, increasing client participation in treatment, and monitoring client progress. In summary, there was high agreement regarding the need for agency policy, training, and procedures to further the linkage of probationer risk and need assessments with treatment programming.

Table 4.

Staff Opinions on Agency and Individual Needs (N = 94).

	Agree or strongly agree (%)	Disagree or strongly disagree (%)
Your agency needs additional guidance in:
Assessing client needs	54.2	16.0
Matching needs with services	67.0	12.8
Increasing program participation by clients	62.4	10.8
Measuring client performance	60.7	10.6
Developing more effective therapies	65.1	8.5
Raising overall quality of counseling	58.5	6.4
Using client assessments to guide clinical and program decisions	60.6	10.6
Using client assessments to document program effectiveness	62.8	7.4
You need more training for:
Assessing client problems and needs	52.2	16.0
Increasing client participation in treatment	48.9	20.2
Monitoring client progress	41.9	29.0

Discussion

This study sought to increase knowledge about probation staff perceptions and attitudes on the role of SUD treatment in probation supervision, innovation, and their organization’s climate for innovation. There was general agreement among probation staff that rehabilitative methods are an appropriate response to crime. Although a promising finding, we also found that line officers were more supportive than management for punishment/deterrence-based responses to crime. This finding mirrors that of Schlager (2008) that parole line staff was more likely to have favorable views of supervision strategies that emphasize sanctions. Although rehabilitative and punishment/deterrence-based responses to crime are not always in direct competition, staff preference for both can result in neutralization and a preference for more punishment approaches in response to relapse or failure to comply with treatment. Agency policy can assist staff on how to balance these two approaches, as part of the skills toolkit needed by line probation staff to respond to drug-using behavior in a way that reinforces the importance of treatment. This is a critical area to ensure that public safety goals reinforce rehabilitative goals while holding the offender accountable.

Officers who supervised specialized drug caseloads were somewhat more supportive of rehabilitative responses than those supervising non-specialized caseloads. This may signify self-selection into this type of role, but probation officers exposed to more treatment-focused caseloads might also learn to place more value on rehabilitative approaches to supervision. It might therefore be of value for agencies to rotate officers onto special drug caseloads to move the overall organizational culture toward more support for treatment-focused supervision. This finding may also reflect additional training on or exposure to treatment practices, suggesting a need to expand such training to officers supervising general caseloads.

Analyses indicated that greater staff endorsement of rehabilitative approaches to crime control was predictive of more openness to innovation and support of EBPs, while stronger support for punishment/deterrence approaches was predictive of lower willingness to adopt EBPs. These findings suggest that organizational innovation and ease of adopting EBPs will be facilitated in probation agencies where staff attitudes are supportive of more rehabilitative approaches in general. Whether agencies can change such attitudes through hiring practices or training is an open question, but one worth exploring (Taxman & Belenko, 2012). Community corrections officers have expressed support for intra-communication among staff and perceive that management supports staff training and development (Rudes et al., 2011), indicating that the door is at least partially open for organizational change that promotes rehabilitation practices to improve referral and placement in treatment.

The results from the EBPAS scales and the scale capturing positive attitudes toward EBPs derived from the MOS (Rieckmann et al., 2007), indicated an overall openness to innovation, agreement that staff would adopt new interventions if required to do so or if the intervention were appealing to them, but moderate agreement with the factor capturing positive attitudes toward EBPs. These findings suggest that if innovations or new EBPs are required and appealing to the staff on different levels, the staff would be more likely to adopt them. ORC items indicated there was by far more agreement than disagreement on the need for additional individual training. Line officers, however, did not agree as strongly as higher level staff that they as individuals needed more training in assessing client problems and needs and in monitoring client progress. Although clinical assessment may not be an appropriate role for line officers, improved recognition of indicators of SUDs could trigger expanded and more appropriate referrals to assessment staff.

Similar to the findings of Rudes et al. (2011), we found only weak agreement that the probation agency is innovative, collaborative, and supportive. There was also weak agreement that the work environment is a priority of the agency, indicating that the staff does not feel as though the agency prioritizes creating a positive work atmosphere. These results suggest an organizational culture problem that could pose a barrier to implementing changes, particularly those that enhance the role of the probation officer as a facilitator of change.

Limitations and Conclusions

Several limitations of this study should be noted. First, although the response rate of this study (about 68%) is relatively high for this type of survey, the fact that 32% of the staff did not respond might limit the generalizability of these findings for this probation department. Unfortunately, because of confidentiality requirements, the research team could not know which staff responded to the survey so it was not possible to compare respondents with non-respondents. Further, no basic demographic data on the probation staff as a whole were available, and thus it not known how the responding sample’s characteristics compared with those of the department as a whole. Another limitation is the high rate of “Don’t Know” responses, so the findings may be missing the opinions of an important subset of staff, or that there is a fair degree of ambivalence in the work environment that is not directly measured and influences probation officer opinions. This limitation is alleviated somewhat by the relatively high overall survey response rate. There is also a concern that respondents could have expressed attitudes or beliefs contrary to their own (i.e., social desirability bias). However, the survey was anonymous and confidential, reducing the likelihood that responses were conditioned by social desirability.

Lastly, this study only captured the perspectives of one urban probation department at one point in time, potentially limiting the generalizability of these findings and their implications for other probation departments. However, it is important to note that there is virtually no existing research on probation staff attitudes and perceptions related to substance abuse treatment and EBPs (Taxman & Belenko, 2012). Thus our findings may be relevant for other probation agencies seeking to implement or expand SUD assessment and treatment, or EBPs in general. We encourage other probation departments to assess these attitudes and beliefs and to move toward increasing our understanding of probation staff attitudes and potential barriers to engaging probationers in treatment.

This study has contributed to the literature by providing new information about probation officer attitudes toward treatment and EBPs, and their perceptions about organizational culture. Probation staff in general seem open to the idea of incorporating treatment into probation supervision, and overall they agree that they would adopt changes if they were required or if they were appealing. However, they do not view the agency as being sufficiently innovative.

Addressing the unmet treatment needs of probationers remains an important goal to improve both public safety and public health. Probation officers can facilitate treatment for SUDs through sound strategies that improve assessment, referral, and treatment matching and linkage. Probation agency leaders, in planning new policies and procedures, need to consider the attitude and values of staff. Staff credentials appear to make a difference in terms of openness to innovation but also in terms of the underlying values. Although this survey did not explore the direct role definition of probation officers (i.e., social workers, behavioral managers, compliance managers), the results suggest that these roles may be important to consider in new policy directives. Moreover, effective collaboration with drug treatment providers requires knowledge, attitudes, and skills that may need to be developed among line officers, especially those who define their roles as focusing on public safety and surveillance, rather than rehabilitation. Expanding access to treatment for probationers should be a priority given the high rates of SUDs among probationers. Effective organizational change toward this goal will need to consider strategies that address staff values and comfort with acquiring new skills. Lessons from recent efforts to train community corrections officers demonstrate the importance of a combined training-organizational strategy that reinforces the importance of new skills (Bonta et al., 2010; Taxman, 2008). Without attention to staff knowledge, attitudes, and perceptions of the overall organizational culture and climate, it is unlikely that new innovations or practices will be sustained.

Footnotes

Appendix A

Appendix B

Acknowledgements

The authors acknowledge the invaluable assistance of Dr. Ellen Kurtz, Former Research Director for the Philadelphia Adult Probation and Parole Department (APPD), as well as the support of Chief Probation and Parole Officer Robert Malvestuto and his successor Charles Hoyt.

Authors’ Note

The views and conclusions expressed in this article solely represent those of the authors and do not necessarily reflect those of the APPD and its staff, NIH, NIMH, or Rutgers University.

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 supported by the National Institutes of Health, National Institute of Mental Health (Grant P30 MH079920) to the Rutgers University Center for Behavioral Health Services and Criminal Justice Research (Dr. Nancy Wolff, principal investigator).

Notes

References

Aarons

G. A.

(2004). Mental health provider attitudes toward adoption of evidence-based practice: The evidence-based practice attitude scale (EBPAS). Mental Health Services Research, 6, 61-74.

Aarons

G. A.

Hurlburt

Horwitz

S. M.

(2011). Advancing a conceptual model of evidence-based practice implementation in public service sectors. Administration and Policy in Mental Health, 38, 4-23.

Aarons

G. A.

McDonald

E. J.

Sheehan

A. K.

Walrath-Greene

C. M.

(2007). Confirmatory factor analysis of the Evidence-Based Practice Attitude Scale (EBPAS) in a geographically diverse sample of community mental health providers. Administration and Policy in Mental Health, 34, 465-469.

American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., Rev.). Washington, DC: Author.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Belenko

Hiller

Hamilton

(2013). Treating substance use disorders in the criminal justice system. Current Psychiatry Reports, 15, 1-11.

Belenko

Peugh

(2005). Estimating drug treatment needs among state prison inmates. Drug and Alcohol Dependence, 77, 269-281.

Blevins

L. D.

Morton

J. B.

(1996). Using the Michigan Alcoholism Screening Test to identify problem drinkers under Federal supervision. Federal Probation, 60, 38-42.

Bonta

Bourgon

Rugge

Scott

T.-L.

Yessine

A. K.

Gutierrez

(2010). The strategic training initiative in community supervision: Risk-need-responsivity in the real world. Ottawa, Ontario: Public Safety Canada.

10.

Caudy

M. S.

Tang

Wooditch

Taxman

F. S.

(2014). Short-term trajectories of substance use in a sample of drug-involved probationers. Journal of Substance Abuse Treatment, 46, 202-213.

11.

Center for Effective Public Policy. (2010). A framework for evidence-based decision making in local criminal justice settings (3rd ed.). Washington, DC: National Institute of Corrections.

12.

Crime and Justice Institute. (2009). Implementing evidence-based policy and practice in community corrections (2nd ed.). Washington, DC: National Institute of Corrections.

13.

Drake

E. K.

(2011). “What works” in community supervision: Interim report (Document No. 11-12-1201). Olympia: Washington State Institute for Public Policy.

14.

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, 24, 364-383.

15.

Evans

Jaffe

Urada

Anglin

M. D.

(2012). Differential outcomes of court-supervised substance abuse treatment among California parolees and probationers. International Journal of Offender Therapy and Comparative Criminology, 56, 539-556.

16.

Evans

Longshore

(2004). Evaluation of the Substance Abuse and Crime Prevention Act: Treatment clients and program types during the first year of implementation. Journal of Psychoactive Drugs, SARC Supplement, 2, 165-174.

17.

Friedmann

P. D.

Hoskinson

Gordon

Schwartz

Kinlock

Knight

. . . Frisman

L. K.

(2012). Medication-assisted treatment in criminal justice agencies affiliated with the Criminal Justice-Drug Abuse Treatment Studies (CJ-DATS): Availability, barriers, and intentions. Substance Abuse, 33, 9-18.

18.

Glaze

L. E.

Kaeble

(2014). Correctional populations in the United States, 2013 (Bureau of Justice Statistics Bulletin, NCJ 248479). Washington, DC: U.S. Department of Justice, Bureau of Justice Assistance.

19.

Knudsen

H. K.

Ducharme

L. J.

Roman

P. M.

Link

(2005). Buprenorphine diffusion: The attitudes of substance abuse treatment counselors. Journal of Substance Abuse Treatment, 29, 95-106.

20.

Krebs

C. P.

Strom

K. J.

Koetse

W. H.

Lattimore

P. K.

(2009). The impact of residential and nonresidential drug treatment on recidivism among drug-involved probationers: A survival analysis. Crime & Delinquency, 55, 442-471.

21.

Lehman

W. E. K.

Greener

J. M.

Simpson

D. D.

(2002). Assessing organizational readiness for change. Journal of Substance Abuse Treatment, 22, 197-209.

22.

Lurigio

Cho

Swartz

Graf

Pickup

(2003). Standardized assessment of substance-related, other psychiatric, and comorbid disorders among probationers. International Journal of Offender Therapy and Comparative Criminology, 47, 630-652.

23.

Maxwell

J. C.

Wallisch

L. S.

(1998). Substance use and crime among probationers in three Texas counties: 1994-1995. Austin, TX: Texas Commission on Alcohol and Drug Abuse.

24.

Mumola

C. J.

Bonczar

T. P.

(1998). Substance abuse and treatment of adults on probation, 1995 (Bureau of Justice Statistics Special Report, NCJ 166611). Washington, DC: U.S. Department of Justice, Bureau of Justice Assistance.

25.

Mumola

C. J.

Karberg

J. C.

(2006). Drug use and dependence, state and federal prisoners, 2004 (Bureau of Justice Statistics Special Report, NCJ 213530). Washington, DC: U.S. Department of Justice, Bureau of Justice Assistance.

26.

Olson

D. E.

Lurigio

A. J.

(2000). Predicting probation outcomes: Factors associated with probation rearrest, revocations, and technical violations during supervision. Justice Research and Policy, 2, 73-86.

27.

Piazza

N. J.

Yeager

R. D.

(1993). Lucas county adult criminal justice alcohol & other drug abuse treatment services needs assessment. Retrieved from https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=145515

28.

Proctor

Landsverk

Aarons

Chambers

Glisson

Mittman

(2009). Implementation research in mental health services: An emerging science with conceptual, methodological, and training challenges. Administration and Policy in Mental Health, 36, 24-34.

29.

Rieckmann

Daley

Fuller

B. E.

Thomas

C. P.

McCarty

(2007). Client and counselor attitudes toward the use of medications for treatment of opioid dependence. Journal of Substance Abuse Treatment, 32, 207-215.

30.

Rudes

D. S.

Lerch

Taxman

F. S.

(2011). Implementing a reentry framework at a correctional facility: Challenges to the culture. Journal of Offender Rehabilitation, 50, 467-491.

31.

Schlager

M. D.

(2008). An assessment of parole officer and administrator attitudes on organizational culture and parole supervision in a Northeastern state. Journal of Offender Rehabilitation, 47, 271-289.

32.

Steiner

Travis

L. F.

III Makarios

M. D.

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

33.

Substance Abuse and Mental Health Services Administration. (2014a). The NSDUH report: Trends in substance use disorders among males aged 18 to 49 on probation or parole. Rockville, MD: Center for Behavioral Health Statistics and Quality.

34.

Substance Abuse and Mental Health Services Administration. (2014b). Results from the 2013 National Survey on Drug Use and Health: Summary of national findings (NSDUH Series H-48, HHS Publication No. (SMA) 14-4863). Rockville, MD: Author.

35.

Taxman

F. S.

(1999). Unraveling “what works” for offenders in substance abuse treatment services. National Drug Court Institute Review, 2, 93-134.

36.

Taxman

F. S.

(2008). No illusion, offender and organizational change in Maryland’s proactive community supervision model. Criminology & Public Policy, 7, 275-302.

37.

Taxman

F. S.

(2009). Effective community punishments in the United States: Probation. Criminal Justice Matters, 75, 42-44.

38.

Taxman

F. S.

Belenko

(2012). Implementing evidence-based practices in community corrections and addiction treatment. New York, NY: Springer.

39.

Taxman

F. S.

Cropsey

K. L.

Young

D. W.

Wexler

(2007b). Screening, assessment, and referral practices in adult correctional settings: A national perspective. Criminal Justice and Behavior, 34, 1216-1234.

40.

Taxman

F. S.

Henderson

Belenko

(2009). Organizational context, systems change, and adopting treatment delivery systems in the criminal justice system. Drug and Alcohol Dependence, 103(Suppl. 1), S1-S6.

41.

Taxman

F. S.

Perdoni

M. L.

Caudy

(2013). The plight of providing appropriate substance abuse treatment services to offenders: Modeling the gaps in service delivery. Victims & Offenders, 8, 70-93.

42.

Taxman

F. S.

Perdoni

M. L.

Harrison

L. D.

(2007a). Drug treatment services for adult offenders: The state of the state. Journal of Substance Abuse Treatment, 32, 239-254.

43.

Taxman

F. S.

Thanner

(2006). Risk, Need, and Responsivity (RNR): It all depends. Crime & Delinquency, 52, 28-51.

44.

Taxman

F. S.

Young

D. W.

Fletcher

B. W.

(2007c). The national criminal justice treatment practices survey: An overview of the special issue. Journal of Substance Abuse Treatment, 32, 221-223.

45.

Young

Farrell

Henderson

Taxman

F. S.

(2009). Filling service gaps: Providing intensive treatment services for offenders. Drug and Alcohol Dependence, 103(Suppl. 1), S33-S42.