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Journal of Correctional Health Care logoLink to Journal of Correctional Health Care
. 2025 Aug 1;31(4):241–251. doi: 10.1089/jchc.24.10.0085

Exploring the Impact of Juvenile Probation Officer’s Individual and Organizational Characteristics on e-Connect Performance

Rosheka N Faulkner 1,*, Alexandra Arnold 2, Casey Sarapas 3, Margaret E Ryan 2, Corianna E Sichel 1,2,4, Gail A Wasserman 1, Faye S Taxman 5, Michael L Dennis 3, Katherine S Elkington 1,2
PMCID: PMC13175259  PMID: 40556604

Abstract

Youths on probation have difficulties accessing care. e-Connect, a clinical decision support system service linkage intervention, reduces these treatment disparities, but it is unclear whether juvenile probation officers’ (JPOs’) individual and organizational characteristics affect the success of e-Connect. Among JPOs using e-Connect, we explore whether attitudes, behaviors, and organizational characteristics related to youths’ behavioral health (BH) treatment are associated with JPO referral practices and treatment initiation among youths. We used weighted, between-group correlations using data from 19 JPOs implementing e-Connect to evaluate self-reported attitudes related to referral and linkage behaviors gathered via survey and administrative data on the service referral and initiation of 78 youths. Female officers were more likely to engage in placement activities (i.e., behaviors aimed at helping youths initiate care), and youths on their caseload were more likely to access BH care. Conversely, officers with more authoritarian attitudes toward supervision were significantly less likely to engage in placement activities. Even within a program found to increase referral 11-fold and linkage to treatment nearly 17-fold, some JPOs fail to make referrals and connect youths to care. To enhance e-Connect’s performance, agencies should target specific attitudes and behaviors around BH services referral and BH initiation.

Keywords: youth, juvenile justice, screening, link to care, probation officers, correctional health care

Introduction

Youths involved in the justice system (YIJ) have higher rates of behavioral health (BH) disorders and service needs than those in the general population (Defoe et al., 2013; Shook et al., 2013). If left untreated, these needs are associated with increased likelihood of further legal system involvement, school dropout, and worsening BH problems, including suicide risk (Beaudry et al., 2021; Hoeve et al., 2013, 2015; McReynolds et al., 2010; Schubert et al., 2011; Teplin et al., 2012). Yet, despite high treatment needs, YIJ are underrepresented in treatment and preventive care. For example, between 50% and 90% of YIJ with treatment need do not access treatment even after their BH need is identified (Aalsma et al., 2017; Kenny et al., 2007; Teplin et al., 2012; Wasserman et al., 2021).

The majority of YIJ are under community supervision (e.g., on probation; Farmer et al., 2003; Puzzanchera et al., 2022), which means that they reside in the community with their caregivers and must access care through community-based systems. Many of these youths, particularly youths of color, have unidentified and/or unaddressed BH needs (Cohen et al., 1990; Dennis et al., 2014; Farmer et al., 2003; Furdella & Puzzanchera, 2015; Rawal et al., 2004). These unmet or undermet needs often result from structural and institutional racism that leads to disparities in access to care (Hamed et al., 2022; Rainer et al., 2023).

These BH needs are important to address as they are identified as being associated with recidivism (Aalsma et al., 2015; Cottle et al., 2001; Pankow et al., 2024), such that youths in treatment are less likely to be rearrested or to penetrate deeper into the system (Evans Cuellar et al., 2006; Henggeler & Schoenwald, 2011; Lipsey, 2009; Pankow et al., 2024). Juvenile probation officers (JPOs) are charged with reducing recidivism, including through the provision of referrals to BH services for youths with identified BH needs (Lee & Taxman, 2020; Pullmann et al., 2006).

Probation, therefore, can be a critical point of intervention to improve identification of service need and uptake of treatment for YIJ. However, in a recent multistate study involving more than 8,000 youths on probation, Wasserman et al. (2021) found that only 20% of the YIJ identified as in need of BH treatment received a referral and approximately 10% initiated treatment. Thus, there is much room for improvement.

Service utilization theories such as the gateway provider model emphasize a multilevel perspective that considers agency, provider, and family/youth-level characteristics that may serve as facilitators and barriers in understanding youths’ BH service use (Stiffman et al., 2004; Wasserman et al., 2009). Research highlights the role of the gateway provider—the individual who initiates or directs the pathway to treatment (Mechanic et al., 1991; Stiffman et al., 2004)—in connecting youths to care (Stiffman et al., 2004; Wasserman et al., 2009). After identifying the need, gateway providers (i.e., JPOs) make decisions based on their services knowledge, attitudes toward treatment, and the availability of services within an organizational context that support or hinder this process (Holloway et al., 2013; Stiffman et al., 2004).

Within probation settings, JPO attitudes about BH services, which may be influenced by past experiences with referral and cross-system collaboration, time, and staff constraints (or lack thereof; Stiffman et al., 2004), can impact their decisions to refer youths to services (Hoffman et al., 2012; Stiffman et al., 2004). JPOs’ authoritarian attitudes (i.e., preference for strict obedience, status clarity, and toughness; Baumrind, 1991) may negatively impact youth treatment initiation (Skeem et al., 2007). For example, the quality of the relationship can predict rule compliance, probation revocation, or arrests (Skeem et al., 2007).

Similarly, JPOs’ behaviors such as using motivational interviewing strategies can encourage behavior change in individuals on their caseload (Clark, 2005). Finally, organizational culture/climate and intra-agency communication can also impact how JPOs interact with their clients (Crittenden & Crittenden, 2008; Glisson & Green, 2006; Heide et al., 2002; Holloway et al., 2013; Nelson et al., 2024). Constructive organizational culture, which supports positive, proactive actions such as showing higher levels of motivation, developing one’s full potential, and supporting others, was associated with an 11-fold increase in youths’ access to care (Glisson & Green, 2006).

Guided by the gateway provider model, e-Connect was developed to address system- and staff-level barriers and increase treatment uptake in youths on probation. e-Connect is a clinical decision support system (CDSS) that aids JPOs in getting youths to BH services. e-Connect quickly identifies youths who need connection to care and helps address transition challenges that exist when helping youths navigate from the justice system to the BH system (Elkington et al., 2023). Youths on probation complete a web-based, evidence-based screening at intake. The screening results are then scored electronically, placing the youth into a crisis or noncrisis range and informing a locally derived referral. Both screening results and resulting referral instructions are provided in real time on the e-Connect tool based on the youth’s scores.

CDSSs have been used in a variety of health systems to help staff handle clinical decision-making and meet clinical guidelines (Johnston et al, 1994; Kawamoto et al., 2005; Sutton et al., 2020). The use of CDSSs can improve patient care by reducing errors and ensuring comprehensive treatment guidelines are followed (Castillo & Kelemen, 2013) and have been shown to improve the quality of client care (Anderson & Willson, 2008; Jaspers et al., 2011), improve cross-system communication (Anderson & Willson, 2008), and improve staff knowledge (Huryk, 2012).

Previous research suggests that there is a connection between the correct use of a CDSS as indicated by the achievement of expected outcomes and individual and organizational characteristics (Mertz et al., 2017; Toth-Pal et al., 2008). However, to our knowledge, no studies have examined the use of a CDSS in a juvenile justice setting by nonclinicians. With respect to e-Connect, correct use is demonstrated by JPO referral of a youth to BH treatment following the identification of needs, and the expected outcome is a youth’s treatment initiation.

Despite e-Connect’s success, not all youths were referred for or initiated treatment, indicating incorrect use of the CDSS by some JPOs. Thus, the goal of this study is to explore the associations between JPOs’ attitudes about practices used to identify/manage BH problems and supervise youths on probation; behaviors that support linkage; characteristics such as gender, age, and education; and perceptions of organizational characteristics (e.g., culture, climate, and job stress), with successful use of a CDSS (e-Connect) as defined by rates of referral and subsequent treatment initiation among youths on probation with BH needs. Findings can inform the development of implementation strategies (e.g., training) to support the successful use of CDSS in probation settings.

Method

Study Design and Participants

Data in the current study are drawn from a larger e-Connect study (Elkington et al., 2023) and the sample comprises 19 JPOs who supervised 78 adjusted juvenile delinquent youths on probation with unmet BH need for 10 counties in a northeastern state. Adjusted juvenile delinquent youths are youths whose cases, following arrest, have been assigned to informal probation supervision rather than formal (family) court adjudication. Youths’ data were drawn from administrative records and collected during a 2-year implementation period from August 1, 2019, through July 30, 2021, as new youths were assigned to probation. JPO data were collected from surveys that were administered 3 months prior to the end of the implementation period.

Before using e-Connect, each JPO completed four, 30-minute web-based trainings focused on (1) suicide behavior and associated risk, (2) e-Connect screening, risk classification, and initiation, (3) using e-Connect, and (4) family engagement with questions generated by the research team. The goal of the training was to improve BH knowledge around suicide behavior, clinical risk classification, referral, and initiation, and to aid JPOs in becoming more familiar with the e-Connect system.

Prior to e-Connect rollout, JPOs within a given county received an in-person, half-day training that reviewed the importance of BH screening and treatment initiation for youths on probation, the county-specific e-Connect referral pathways, how to engage youths and families, how to utilize the e-Connect system, and how to enter screening, referral, and treatment initiation data into the county statewide Management Information System (MIS).

To ensure fidelity to the e-Connect project, fidelity monitoring via random surveys to JPOs occurred throughout the project. In addition, live consultation (by phone or email) was provided to JPOs to address any unforeseen circumstances in referral, ensuring youths made it to care as intended and to monitor/support fidelity to pathway protocols. All study activities were approved by the New York State Psychiatric Institute’s institutional review board.

Probation Staff

JPO data were drawn from a staff survey measuring attitudes and behaviors. A total of 87 JPOs were enrolled in the e-Connect project. For the current study, JPOs were included only if they completed a staff survey (n = 58) and had at least one youth who screened as noncrisis but in need of BH services on their caseload (n = 19); the size of the final JPO sample was 19 (see Fig. 1 for more information on the sample breakdown for JPOs). JPOs were recruited via email following orientation meetings in their county for the e-Connect project, during which details about the investigation as well as the e-Connect system itself were explained.

Fig. 1.

Fig. 1.

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Youth and juvenile probation officer consort table.

Youths on Probation

Youths’ data for this study were captured from two main sources: administrative data records and e-Connect BH screening data. Data were drawn from the administrative data records of 6,442 youths on probation collected between August 1, 2019, and July 30, 2021, which included 988 youths eligible to receive the e-Connect screening. Our prior work examined the efficacy of the e-Connect system over the implementation period in increasing referral and initiation rates. It is important to note that the previous study found referral and initiation results remained unchanged during the pre- and post-COVID-19 shutdown periods (see Elkington et al., 2023 for more details).

Only youths whose screening results classified them as having current BH treatment needs, not currently in crisis (n = 188), who were not currently in treatment (n = 152), who were on the caseload of one of the 19 JPOs selected for this analysis (n = 93), and did not have missing data were included—a total of 78 youths on probation. Only noncrisis youths were included in the study because the cases of youths in crisis received a higher level of oversight provided by supervisors, with support from research staff to ensure their referral and initiation into care. Details of how youths on probation were selected for the final sample can be found in Figure 1.

Families and caregivers play an important role in e-Connect. They agree to the release of e-Connect screening data to a BH provider and provide support in getting youths to treatment. Although the onus is on youths and their families to initiate care, the JPO has a responsibility to help encourage a youth and their family to initiate treatment and to communicate with the BH provider (assuming a release has been signed) to verify whether the youth attended the first appointment, preventing the youth from falling between two systems (Elkington et al., 2020).

Assessments: Youths’ Data

Youths’ demographics, referral, and treatment initiation

In order to track the BH needs of YIJ and their access to care, JPOs use the statewide MIS to document administrative information on the youth’s case. This information includes youth demographics (sex, race, and age) and youths’ outcome information including screening results, service referrals, and treatment initiation outcomes. JPOs are trained on MIS data entry so that they understand what is considered treatment referral and initiation and where to enter this information in the MIS.

Treatment referral is defined as the date the JPO provided a specific treatment referral to the youth and family following the result of the e-Connect screening, as determined by the referral pathway. Treatment initiation is determined by confirmation from the JPO that the youth has attended at least one BH session after screening with e-Connect. JPOs have regular contact with the youths, families, and providers that enable them to collect this information.

To help de-identify youths’ demographic information, age was provided in years, and youths’ race was provided as White/youth of color.

Youths’ BH need

Data on youths’ BH need and suicidal behavior are drawn from the e-Connect screening hosted by Chestnut Health Systems using the GAIN-Short Screener’s internalizing and substance use subscales and six items from the GAIN-I that capture suicidal behavior and nonsuicidal self-injury (Dennis et al., 2003, 2013). Both instruments are well-validated with youths (Ives et al., 2010; for more detail on identification of youths’ BH need, please see Elkington et al., 2023; Sarapas et al., 2025; and Wasserman et al., 2021).

Assessments: Staff Data

Staff attitudes, behaviors, and perceptions of organizational characteristics

JPOs were asked to complete a survey documenting demographic information (e.g., age, gender, and years working for this employer) and examining their knowledge of youths’ BH risk and behaviors and attitudes around youths’ mental health problems, treatment, and the JPO’s organization. This postsurvey was administered at the end of the study period. This survey had a 91% completion rate. Table 1 identifies the staff survey variables and a brief description of each variable.

Table 1.

Staff Survey Variable Description

Description JPO variable
Attitudes about practices used to supervise youths on probation Use of authoritarian (vs. authoritative) techniques
Evidence-based standardized screening
Youth-affirming motivational interviewing
Satisfaction with agency’s behavioral-health-oriented practices
Self-report of behaviors Monitoring youth’s treatment progress
Ensuring placement of youth with behavioral health need in treatment
Attitudes about the e-Connect system Caseload explorer utility; project’s data entry system
Working with e-Connect system
Usefulness of e-Connect referral form
Staff perception of organization-level factors Organizational characteristics
Organizational climate
Organizational stress
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The left column of this table describes the categories of JPO variables used in this study. The right side of the column briefly describes the specific variables that are aligned with these categories.

JPO = juvenile probation officer.

Attitudes.

The survey used 5-point Likert scale questions to assess support for the use of standardized screening (nine items; e.g., “a brief, scored screen at intake is the best way to identify youths who need further formal standardized assessment”); authoritarian communication techniques that emphasize a commanding style of communication (seven items; e.g., “reiterate to the youth an area that they should desire to change”); encouraging communication techniques (eight items derived from a motivational interviewing scale that are used in this study to measure JPOs’ communication strategies with youths; e.g., “praise the youth for a successful completion of a task or achieving a goal”); and satisfaction with agency’s BH-oriented practices (seven items; e.g., “your agency’s current BH screening procedures”).

Attitude questions were adapted from the Juvenile Assessment, Referral, Placement, and Treatment Planning Protocol scale (Taxman et al., 2012).

Behaviors

JPO’s monitoring (three items; e.g., “face-to-face contact with youth’s family”) and placement behaviors (six items; e.g., “scheduling date/time for youth’s initial appointment” or “establish a schedule with provider to report youth’s progress”) used 5-point Likert scales of frequency (1 = never to 5 = weekly or more and 1 = never to 5 = always, respectively). These scales were designed to measure the frequency or extent to which JPOs were engaging in specific activities or procedures and were adapted from the Juvenile Service-Oriented Practice Scale (Farrell et al., 2011).

Organization

JPO views on their intra-agency communication (three items; e.g., “employees are always kept well informed”) and job-related stress (four items; e.g., “staff members often show signs of stress and strain”) were measured by questions using a Likert scale from 1 (strongly disagree) to 4 (strongly agree). These questions measured the degree to which JPOs agreed with the statements provided about their agency.

These questions were adapted from JJ-TRIALS, an implementation science project designed to connect YIJ in need of substance use services to community care (Knight et al., 2016, 2019). Items for the organizational climate questions were adapted from National Criminal Justice Treatment Practices survey, which were in turn adapted from Orthner et al., 2006; Scott & Bruce, 1994; and Taxman et al., 2007.

Data Analysis

Analyses were conducted in R 4.4.0 software (https://www.R-project.org/). Summary statistics were computed for sample characteristics and overall initiation and referral rates.

To understand how staff characteristics affected referral and initiation outcomes, we computed between-group, point-biserial correlations of each staff-level characteristic with youths’ outcomes. Specifically, for referral we examined correlation of staff characteristics with a binary indicator of whether each in-need youth had a documented treatment referral. For initiation, among youths who received referrals (n = 65 youths from 17 JPOs), we examined correlations with an indicator of whether each referred youth had documented initiation of treatment. Because our data had a nested structure—i.e., youths nested within JPOs—the assumption of independence for simple correlations was not met.

We therefore computed between-group correlations as described by Dansereau et al. (1984) and Pedhazur (1997) and implemented in the R psych package (Revelle, 2022). This is a multilevel approach that provides an estimate of how lower-level outcomes (e.g., youth referral and initiation) vary as a function of higher-level factors (e.g., JPO characteristics), accounting for nesting. It is analogous to a correlation between JPO characteristics and youths’ referral and initiation rates within each JPO, weighted by number of youths per JPO. An alternative approach, using binomial mixed models to predict outcomes with random intercepts for JPO, yielded similar results to those presented below.

Results

Sample Characteristics

Table 2 presents demographic characteristics of both JPOs and the youths on their probation caseload. JPOs were largely white (95%) women (79%) with a bachelor’s degree (74%) and a mean age of 41.6 years old (standard deviation [SD] = 8.8). JPOs had an average of 8.8 years working with their agency (SD = 7.7) and 5.4 years supervising youths on probation (SD = 4.7). The majority of youths on probation were white (55%) and male (55%), with an average age of 15.99 years (SD = 2.0).

Table 2.

Youth and Juvenile Probation Officer Demographics

Demographics N a Percent
Youth sex
 Female 33 41%
 Male 43 55%
 Missing 2 4%
Youth race
 White 43 58%
 Youth of color 28 31%
 Missing 7
Youth age
Mean 15.99
SD: 1.997
Range: 10–19		  78
Juvenile probation officer gender
 Female 15 79%
 Male 4 21%
Juvenile probation officer race
 White 18 95%
 Other 1 5%
Juvenile probation officer age
Mean: 41.59
SD: 8.762
Range: 28–58		  19
Highest level of education juvenile probation officers achieved
 Bachelor’s 14 74%
 Master’s 5 26%
Juvenile probation officers’ years working for employer
Mean: 8.82
SD: 7.708
Range: 1–23		  18b
Juvenile probation officers’ years supervising youths on probation
Mean: 5.42
SD: 4.714
Range: 0–18		  19
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a

n = 78 for youth and n = 19 for juvenile probation officer.

b

Data not available for one juvenile probation officer.

SD = standard deviation.

Overall Rates of Referral and Initiation

Figure 2 displays the distribution of the proportion of youths on each JPO’s caseload who were referred and who initiated treatment, based on JPO caseload size. Each circle represents a single JPO, and the size of each circle represents the size of that JPO’s caseload.

Fig. 2.

Fig. 2.

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Referral and initiation distribution. JPO = juvenile probation officer.

Referral

Most (68%, n = 13) JPOs referred 100% of the youths identified on their caseload as noncrisis but in need of BH services. Approximately 21% of JPOs (n = 4) referred 40–60% of youths on their caseload, whereas 11% (n = 2) JPOs did not refer any of the noncrisis youths in need of BH services. Overall, 83% (n = 65) of noncrisis youths with previously unmet BH need were referred.

Initiation

Among the JPOs who referred any of the youths on their caseload to services (n = 17), initiation rates varied widely. For seven JPOs (41%), 100% of noncrisis youths with BH need who were referred initiated services. For three JPOs (18%), none of the noncrisis youths with need who were referred initiated services. Overall, 78% (n = 51) of noncrisis youths with BH need who were referred to services initiated care.

Correlates of Referral and Initiation

Table 3 shows associations between JPO-level variables (demographics, attitudes, behaviors, and perception of organizational characteristics) and the rate of referral and initiation among youths.

Table 3.

Correlation Between Juvenile Probation Officer-Level Variables and Rates of Referral and Initiation

Youths referred Youths initiated
JPO variable n JPOs n youths r p n JPOs n youths r p
Age 19 78 0.120 0.625 17 65 −0.094 0.721
Femalea 19 78 0.541 0.017 17 65 0.689 0.002
Years working for employer 18 68 0.020 0.937 16 55 −0.400 0.125
Years providing case management supervision for youths on probation 19 78 0.055 0.823 17 65 −0.087 0.739
Caseload 19 78 −0.060 0.808 17 65 −0.113 0.665
Master’s 19 78 −0.021 0.933 17 65 0.259 0.316
Standardized screening 18 69 0.163 0.518 16 56 0.071 0.794
Motivational interviewing 18 69 0.164 0.516 16 56 0.248 0.353
Authoritarian techniques 18 69 −0.524 0.025 16 56 −0.358 0.174
Practices to ensure behavioral health placement 18 69 0.132 0.600 16 56 0.493 0.052
Satisfaction with behavioral-health-oriented practices 18 69 −0.080 0.752 16 56 0.256 0.339
Youth monitoring 18 69 0.132 0.603 16 56 −0.102 0.707
Caseload explorer utility 18 69 0.190 0.451 16 56 0.315 0.235
Organizational characteristics 18 69 −0.252 0.313 16 56 0.341 0.196
Organizational climate 18 69 −0.007 0.978 16 56 0.333 0.208
Organizational stress 18 69 0.355 0.149 16 56 0.052 0.848
e-Connect systems 18 69 0.094 0.710 16 56 0.248 0.354
e-Connect device 18 69 0.052 0.839 16 56 0.130 0.631
e-Connect referral form 18 69 −0.149 0.556 16 56 0.029 0.916
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Values are between-group correlations for effects of JPO-level traits on youth-level outcomes, with youths nested within JPOs. Bold indicates significance at p < 0.05; italic indicates trend significance at 0.05 ≤ p < 0.10.

a

Female is the default category. There are a total of 15 females and 4 males in the sample.

JPO = juvenile probation officer.

Referral

Referral rates were higher among female JPOs (r = 0.541, p = 0.017) and lower among JPOs who endorsed positive attitudes toward authoritarian techniques (r = −0.524, p = 0.025). JPOs’ view of organizational characteristics was unrelated to rates of referral. Other variables such as years of experience or opinions about the CDSS were not statistically significant.

Treatment initiation

The initiation rate was higher for youths with female JPOs (r = 0.689; p = 0.002) and a trend toward those JPOs who reported engaging in behaviors to support BH placement (r = 0.493, p = 0.052). JPOs’ view of organizational characteristics was also unrelated to rates of initiation. All other variables were not significant, and we explore some potential reasons for some of these null findings in the discussion section.

Discussion

This is one of a few studies to examine the relationship between JPOs’ individual (e.g., attitudes and behaviors) and organizational characteristics and their correct use of a CDSS (e-Connect) designed to enhance referral and initiation rates for youths on probation with BH needs. e-Connect is one of the first CDSSs to integrate actions to support screening, referral, and treatment initiation of youths on probation. This study contributes to the literature by broadening the examination of CDSS technology use in a nonclinical (juvenile probation) setting, exploring the staff and organization-level factors that enhance or limit successful use of a CDSS.

Among JPOs using e-Connect, female JPOs had higher rates of referral to BH services. Conversely, JPOs with authoritarian attitudes toward youth supervision had lower rates of referral. Among youths who were referred to BH services, initiation rates were higher among youths supervised by female JPOs and youths whose JPO engaged in behaviors to aid treatment placement, such as helping youths schedule their initial appointment. These findings are consistent with prior studies that have examined the influence of JPO attitudes and behaviors on referral and treatment initiation, both with (De Vries, 2013; Goud et al., 2010; Toth-Pal et al., 2008) and without (Diamond et al., 2012; Skeem et al., 2007; Stiffman et al., 2004; Wasserman et al., 2009) the use of a CDSS.

Nonetheless, the work on the impact of staff and organizational factors on CDSS implementation is limited, making it difficult to compare this work to a larger body of previous studies. Future work should study other JPO and organizational variables (e.g., staff knowledge of the CDSS and attitudes toward the use of technology) to explore their potential impact on the successful implementation of CDSSs.

There was no observed relationship between organizational characteristics (intra-agency communication, organizational climate, and organizational stress) and either JPOs’ referral or youths’ treatment initiation. This finding is inconsistent with the limited research in this area, which suggests the importance of organizational management, climate, and other characteristics to the acceptance of a CDSS (Chang et al., 2007; Goud et al., 2010). Reasons for current study findings may be threefold. First, because the counties involved in this study all volunteered to participate in e-Connect, it is possible that they share similar organizational characteristics (e.g., an open and supportive relationship with management), therefore reducing variability in measures of organizational climate and culture.

Second, it is also possible that, because of the systematic nature of e-Connect where a direct and clearly defined process is established to screen, refer, and link youth to treatment, the e-Connect system was able to mitigate the impact of organizational characteristics measured in the current study on referral and treatment outcomes.

Third, organizational variables not measured in the current study, such as interagency collaboration and staff burnout, are more pertinent to the use of CDSSs that promote BH need identification, referral, and treatment initiation. Further research should explore the impact of these additional organizational variables and the importance of their role in CDSS use and associated outcomes. In addition, research should explore whether organizational willingness and readiness to utilize a CDSS impact the success of implementation.

This study also found that JPO-level factors such as years of work experience, case management experience, caseload size, and education level did not impact youths’ referral and initiation rate. This finding is promising as it suggests that JPOs with less education, less experience, and higher caseloads are not disadvantaged in making referrals and assisting youths with initiation when using e-Connect. Study results also suggest that efforts to improve referral and treatment initiation outcomes associated with the use of a CDSS such as e-Connect may benefit from fostering JPOs’ authoritative attitudes (thus lowering authoritarian ones) and improving JPO behaviors that aid placement of youths in treatment, as these factors are correlated with successful referral and treatment initiation.

In particular, providing training to JPOs that is aimed at reducing support for authoritarian, command-based communication styles and instead promoting more encouraging communication strategies such as those used in motivational interviewing (Rollnick & Miller, 1995) or rehabilitative-oriented strategies (Clear & Latessa, 1993; Steiner et al., 2011) might aid implementation success. Increasing behaviors that aid treatment placement might be challenging however, considering research suggests limited support for this practice among probation staff (Knight et al., 2019).

Targeting perceived role relevance (i.e., the importance of an officer’s position in facilitating a youth’s care) in combination with training on active treatment initiation behaviors (e.g., assisting family in making an appointment; following up with the BH provider to ensure a youth attended treatment) might better support youths’ access to care through e-Connect (Belenko et al., 2017; Manning et al., 2012; Zimmermann et al., 2018).

JPO perceptions of youths’ treatment need in the context of their gender and race/ethnicity (Farmer et al., 2003; Lopez-Williams et al., 2006; Teplin et al., 2005) have been shown to affect JPO screening and referral behaviors (Ryan et al., 2023; Wasserman et al., 2008). For example, minority youths and boys are less likely to be perceived as in need of treatment, which translates into lower levels of referral and treatment initiation, despite service need (Lopez-Williams et al., 2006; Zeola et al., 2017). Characteristics such as staff age, tenure on the job, and education level can also impact their likelihood of turnover, and rate of turnover can negatively impact the implementation of an evidence-based practice (Woltmann et al., 2008).

Recent research looking at e-Connect study participants found that, at baseline, girls of color were less likely to get referred to treatment, but the use of e-Connect was able to mitigate this issue (Ryan et al., 2023). It is unclear whether e-Connect has the same mitigating impact on JPOs’ race and gender. Future research should take a more nuanced approach to understand whether associations between a JPO’s attitudes and behavior and e-Connect outcomes differ by youths’ race, gender, or ethnicity.

Limitations

While our study offers important insights into the potential effects of individual and organizational factors on JPO referral behaviors and youths’ treatment initiation, several limitations of this study should be acknowledged. Only a small number of JPOs had available data (n = 19), which reduces the reliability of estimated effects of JPO traits. As such, we were only able to detect significant effects with large magnitudes (i.e., all significant effects had an |r| value of more than 0.5). Replication in a larger sample may identify additional staff-level factors that affect youths’ referral and initiation. The small number of JPOs also prevented us from examining or accounting for potential county-level effects.

Since this study only considered JPOs and youths on probation, its results might not be generalizable to other juvenile justice settings. Because JPO-level variables were based on self-report, there is a possibility for social desirability bias where JPOs might provide responses that are desirable rather than those that are accurate. JPO attitudes and behaviors were gathered 3 months prior to the end of the 2-year implementation phase, while outcome variables (youths’ referral and initiation rates) were gathered across the 2-year implementation phase period. Thus, we cannot claim causation but only correlation between staff attitudes, behaviors, and organizational factors, and successful use of e-Connect as determined by JPOs’ referrals and youths’ BH treatment initiation.

There were only four male JPOs, and 95% of all JPOs were white. The sample of JPOs lacks diversity and, unfortunately, does not reflect the demographic makeup of youths on probation. However, this demographic homogeneity is representative of the characteristics of JPOs within the northeastern state in which the study was conducted. We did not gather data on youth/family-level variables (e.g., stigma, systems mistrust, transportation, and housing instability) that may have also influenced access to treatment in this sample. Finally, missing values in administrative data might have resulted in modest referral and treatment initiation outcomes.

Conclusion

YIJ, including those on probation, face challenges accessing BH care (Elkington et al, 2020), and research suggests that the use of a CDSS might help systematize and improve access to treatment (Anderson & Willson, 2008; Jaspers et al., 2011; Ryan et al., 2023). While using e-Connect, a CDSS aimed at connecting youths on probation to BH care, JPOs’ attitudes, behaviors, and characteristics are identified as being associated with youths’ referral to treatment by JPOs and the youths’ treatment initiation rate. CDSSs focused on closing the treatment gap for YIJ through JPO supervision should seek to address these factors that could impact implementation success.

Acknowledgments

The authors are grateful to the probation officers and other probation partners for their participation in this project. It would not have been possible without their support, both at the state and local levels.

Authors’ Contributions

G.A.W., K.S.E., C.E.S., F.S.T., and M.L.D. conceived the research and obtained the funding. R.N.F., A.A., M.E.R., C.E.S., G.A.W., and K.S.E. implemented the study. R.N.F., A.A., C.S., M.E.R., C.E.S., M.L.D., and K.S.E. analyzed the data and assisted with interpretation of findings. R.N.F., A.A., M.E.R., C.E.S., G.A.W., and K.S.E. drafted the initial article. All authors approved the final submitted version.

Author Disclosure Statement

The authors disclosed no conflicts of interest with respect to the research, authorship, or publication of this article.

Funding Information

This research was supported by the National Institute of Mental Health: (R01 MH113599, PI: K.S.E.). The findings and conclusions in this article are those of the authors and do not represent the official position of the National Institute of Mental Health.

References

  1. Aalsma M. C., , Anderson V. R., , Schwartz K., , Ouyang F., , Tu W., , Rosenman M. B., , & Wiehe S. E. (2017). Preventive care use among justice-involved and non-justice-involved youth. Pediatrics, 140(5). 10.1542/peds.2017-1107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Aalsma M. C., , White L. M., , Lau K. S., , Perkins A., , Monahan P., , & Grisso T. (2015). Behavioral health care needs, detention-based care, and criminal recidivism at community reentry from juvenile detention: A multisite survival curve analysis. American Journal of Public Health, 105(7), 1372–1378. 10.2105/ajph.2014.302529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Anderson J. A., , & Willson P. (2008). Clinical decision support systems in nursing: Synthesis of the science for evidence-based practice. Computers, Informatics, Nursing, 26(3), 151–158. 10.1097/01.NCN.0000304783.72811.8e [DOI] [PubMed] [Google Scholar]
  4. Baumrind D. (1991). Influence of parenting style on adolescent competence and substance use. The Journal of Early Adolescence, 11(1), 56–95. 10.1177/0272431691111004 [Google Scholar]
  5. Beaudry G., , Yu R., , Långström N., , & Fazel S. (2021). An updated systematic review and meta-regression analysis: Mental disorders among adolescents in juvenile detention and correctional facilities. Journal of the American Academy of Child and Adolescent Psychiatry, 60(1), 46–60. 10.1016/j.jaac.2020.01.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Belenko S., , Knight D., , Wasserman G. A., , Dennis M. L., , Wiley T., , Taxman F. S., , Oser C., , Dembo R., , Robertson A. A., , & Sales J. (2017). The Juvenile Justice Behavioral Health Services Cascade: A new framework for measuring unmet substance use treatment services needs among adolescent offenders. Journal of Substance Abuse Treatment, 74, 80–91. 10.1016/j.jsat.2016.12.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Castillo R. S., , & Kelemen A. (2013). Considerations for a successful clinical decision support system. Computers, Informatics, Nursing, 31(7), 319–326. 10.1097/NXN.0b013e3182997a9c [DOI] [PubMed] [Google Scholar]
  8. Chang I.-C., , Hwang H.-G., , Hung W.-F., , & Li Y.-C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296–303. 10.1016/j.eswa.2006.05.001 [Google Scholar]
  9. Clark, M. D. (2005). Motivational interviewing for probation staff: Increasing the readiness to change (part I). Federal Probation, 69(22). https://www.uscourts.gov/sites/default/files/69_2_5_0.pdf [Google Scholar]
  10. Clear T. R., , & Latessa E. J. (1993). Probation officers’ roles in intensive supervision: Surveillance versus treatment. Justice Quarterly, 10(3), 441–462. 10.1080/07418829300091921 [Google Scholar]
  11. Cohen R., , Parmelee D. X., , Irwin L., , Weisz J. R., , Howard A., , Purcell P., , & Best A. M. (1990). Characteristics of children and adolescents in a psychiatric hospital and a corrections facility. Journal of the American Academy of Child and Adolescent Psychiatry, 29(6), 909–913. 10.1097/00004583-199011000-00012 [DOI] [PubMed] [Google Scholar]
  12. Cottle C. C., , Lee R. J., , & Heilbrun K. (2001). The prediction of criminal recidivism in juveniles: A meta-analysis. Criminal Justice and Behavior, 28(3), 367–394. 10.1177/0093854801028003005 [Google Scholar]
  13. Crittenden V. L., , & Crittenden W. F. (2008). Building a capable organization: The eight levers of strategy implementation. Business Horizons, 51(4), 301–309. 10.1016/j.bushor.2008.02.003 [Google Scholar]
  14. Dansereau F., , Alutto J. A., , & Yammarino F. J. (1984). Theory testing in organizational behavior: The varient approach. Prentice-Hall. [Google Scholar]
  15. De Vries A. E., , Van Der Wal M. H. L., , Nieuwenhuis M. M. W., , De Jong R. M., , Van Dijk R. B., , Jaarsma T., , Hillege H. L., , & Jorna R. J. (2013). Perceived barriers of heart failure nurses and cardiologists in using clinical decision support systems in the treatment of heart failure patients. BMC Medical Informatics and Decision Making, 13(1), 54. 10.1186/1472-6947-13-54 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Defoe I. N., , Farrington D. P., , & Loeber R. (2013). Disentangling the relationship between delinquency and hyperactivity, low achievement, depression, and low socioeconomic status: Analysis of repeated longitudinal data. Journal of Criminal Justice, 41(2), 100–107. 10.1016/j.jcrimjus.2012.12.002 [Google Scholar]
  17. Dennis M. L., , Clark H. W., , & Huang L. N. (2014). The need and opportunity to expand substance use disorder treatment in school-based settings. Advances in School Mental Health Promotion, 7(2), 75–87. 10.1080/1754730x.2014.888221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dennis M. L., , Titus J. C., , White M. K., , Unsicker J. I., , & Hodgkins D. (2003). Global appraisal of individual needs: Administration guide for the GAIN and related measures. Chestnut Health Systems. [Google Scholar]
  19. Dennis M. L., , Feeney T., , & Titus J. C. (2013). Global Appraisal of Individual Needs-Short Screener (GAIN-SS): Administration and scoring manual, Version 3. Chestnut Health Systems. https://www.hca.wa.gov/assets/billers-and-providers/gain-ss-manual-v3.0.pdf [Google Scholar]
  20. Diamond G. S., , O’Malley A., , Wintersteen M. B., , Peters S., , Yunghans S., , Biddle V., , O’Brien C., , & Schrand S. (2012). Attitudes, practices, and barriers to adolescent suicide and mental health screening. Journal of Primary Care & Community Health, 3(1), 29–35. 10.1177/2150131911417878 [DOI] [PubMed] [Google Scholar]
  21. Elkington K. S., , Lee J., , Brooks C., , Watkins J., , & Wasserman G. A. (2020). Falling between two systems of care: Engaging families, behavioral health and the justice systems to increase uptake of substance use treatment in youth on probation. Journal of Substance Abuse Treatment, 112, 49–59. 10.1016/j.jsat.2020.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Elkington K. S., , Wasserman G. A., , Ryan M. E., , Sichel C. E., , Sarapas C., , Dennis M. L., , Taxman F. S., , & Cuijpers P. (2023). e-Connect: Linking probation youth at risk for suicide to behavioral health services. Journal of Consulting and Clinical Psychology, 91(9), 547–557. 10.1037/ccp0000824 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Evans Cuellar A., , McReynolds L. S., , & Wasserman G. A. (2006). A cure for crime: Can mental health treatment diversion reduce crime among youth? Journal of Policy Analysis and Management, 25(1), 197–214. 10.1002/pam.20162 [DOI] [PubMed] [Google Scholar]
  24. Farmer E. M. Z., , Burns B. J., , Phillips S. D., , Angold A., , & Costello E. J. (2003). Pathways into and through mental health services for children and adolescents. Psychiatric Services, 54(1), 60–66. 10.1176/appi.ps.54.1.60 [DOI] [PubMed] [Google Scholar]
  25. Farrell J. L., , Young D. W., , & Taxman F. S. (2011). Effects of organizational factors on use of juvenile supervision practices. Criminal Justice and Behavior, 38(6), 565–583. 10.1177/0093854811401786 [Google Scholar]
  26. Furdella J., , & Puzzanchera C. (2015). Delinquency cases in juvenile court, 2013. Office of Juvenile Justice and Delinquency Prevention. https://ojjdp.ojp.gov/sites/g/files/xyckuh176/files/pubs/248899.pdf [Google Scholar]
  27. Glisson C., , & Green P. (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, 33(4), 433–448. 10.1007/s10488-005-0016-0 [DOI] [PubMed] [Google Scholar]
  28. Goud R., , van Engen-Verheul M., , de Keizer N. F., , Bal R., , Hasman A., , Hellemans I. M., , & Peek N. (2010). The effect of computerized decision support on barriers to guideline implementation: A qualitative study in outpatient cardiac rehabilitation. International Journal of Medical Informatics, 79(6), 430–437. 10.1016/j.ijmedinf.2010.03.001 [DOI] [PubMed] [Google Scholar]
  29. Hamed S., , Bradby H., , Ahlberg B. M., , & Thapar-Bjorkert S. (2022). Racism in healthcare: A scoping review. BMC Public Health, 22(1), 988. 10.1186/s12889-022-13122-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Heide M., , Grønhaug K., , & Johannessen S. (2002). Exploring barriers to the successful implementation of a formulated strategy. Scandinavian Journal of Management, 18(2), 217–231. 10.1016/S0956-5221(01)00007-0 [Google Scholar]
  31. Henggeler S. W., , & Schoenwald S. K. (2011). Evidence-based interventions for juvenile offenders and juvenile justice policies that support them. Social Policy Report, 25(1), 1–28. 10.1002/j.2379-3988.2011.tb00066.x [Google Scholar]
  32. Hoeve M., , Colins O. F., , Mulder E. A., , Loeber R., , Stams G. J. J. M., , & Vermeiren R. R. J. M. (2015). The association between childhood maltreatment, mental health problems, and aggression in justice-involved boys. Aggressive Behavior, 41(5), 488–501. 10.1002/ab.21586 [DOI] [PubMed] [Google Scholar]
  33. Hoeve M., , McReynolds L. S., , Wasserman G. A., , & McMillan C. (2013). The influence of mental health disorders on severity of reoffending in juveniles. Criminal Justice and Behavior, 40(3), 289–301. 10.1177/0093854812459639 [Google Scholar]
  34. Hoffman K. A., , Green C. A., , Ford, , J. H. II, , Wisdom J. P., , Gustafson D. H., , & McCarty D. (2012). improving quality of care in substance abuse treatment using five key process improvement principles. The Journal of Behavioral Health Services & Research, 39(3), 234–244. 10.1007/s11414-011-9270-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Holloway E. D., , Brown J. R., , Suman P. D., , & Aalsma M. C. (2013). A qualitative examination of juvenile probation officers as gateway providers to mental health care. Criminal Justice Policy Review, 24(3), 370–392. 10.1177/0887403412436603 [Google Scholar]
  36. Huryk L. A. (2012). Information Systems and Decision Support Systems. American Journal of Nursing, 112(1), 62–65. 10.1097/01.naj.0000410365.51061.81 [DOI] [PubMed] [Google Scholar]
  37. Ives M. L., , Chan Y. F., , Modisette K. C., , & Dennis M. L. (2010). Characteristics, needs, services, and outcomes of youths in juvenile treatment drug courts as compared to adolescent outpatient treatment. Drug Court Review, 7(1), 10–56. [Google Scholar]
  38. Jaspers M. W. M., , Smeulers M., , Vermeulen H., , & Peute L. W. (2011). Effects of clinical decision-support systems on practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings. Journal of the American Medical Informatics Association, 18(3), 327–334. 10.1136/amiajnl-2011-000094 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Johnston M. E., , Langton K. B., , Haynes R. B., , & Mathieu A. (1994). Effects of computer-based clinical decision support systems on clinician performance and patient outcome: A critical appraisal of research. Annals of Internal Medicine, 120(2), 135–142. 10.7326/0003-4819-120-2-199401150-00007 [DOI] [PubMed] [Google Scholar]
  40. Kawamoto K., , Houlihan C. A., , Balas E. A., , & Lobach D. F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ, 330(7494), 765–768. 10.1136/bmj.38398.500764.8F [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kenny D. T., , Lennings C. J., , & Nelson P. K. (2007). The mental health of young offenders serving orders in the community: Implications for rehabilitation. Journal of Offender Rehabilitation, 45(1–2), 123–148. 10.1300/J076v45n01_10 [Google Scholar]
  42. Knight D. K., , Belenko S., , Wiley T., , Robertson A. A., , Arrigona N., , Dennis M., , Bartkowski J. P., , McReynolds L. S., , Becan J. E., , Knudsen H. K., , Wasserman G. A., , Rose E., , DiClemente R., , Leukefeld C., , Brody G., , Cawood M., , Chandler R., , Dembo R., , Donohue P., , … Zibalese-Crawford M. (2016). Juvenile justice-translational research on interventions for adolescents in the legal system (JJ-TRIALS): A cluster randomized trial targeting system-wide improvement in substance use services. Implementation Science, 11(1), 57. 10.1186/s13012-016-0423-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Knight D. K., , Joe G. W., , Morse D. T., , Smith C., , Knudsen H., , Johnson I., , Wasserman G. A., , Arrigona N., , McReynolds L. S., , Becan J. E., , Leukefeld C., , & Wiley T. R. A. (2019). Organizational context and individual adaptability in promoting perceived importance and use of best practices for substance use. The Journal of Behavioral Health Services & Research, 46(2), 192–216. 10.1007/s11414-018-9618-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lee J. S., , & Taxman F. S. (2020). Using latent class analysis to identify the complex needs of youth on probation. Children and Youth Services Review, 115, 105087. 10.1016/j.childyouth.2020.105087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lipsey M. W. (2009). The primary factors that characterize effective interventions with juvenile offenders: A meta-analytic overview. Victims & Offenders, 4(2), 124–147; 10.1080/15564880802612573 [Google Scholar]
  46. Lopez-Williams A., , Stoep A. V., , Kuo E., , & Stewart D. G. (2006). Predictors of mental health service enrollment among juvenile offenders. Youth Violence and Juvenile Justice, 4(3), 266–280. 10.1177/1541204006290159 [Google Scholar]
  47. Manning V., , Best D., , Faulkner N., , Titherington E., , Morinan A., , Keaney F., , Gossop M., , & Strang J. (2012). Does active referral by a doctor or 12-step peer improve 12-step meeting attendance? Results from a pilot randomised control trial. Drug and Alcohol Dependence, 126(1–2), 131–137. 10.1016/j.drugalcdep.2012.05.004 [DOI] [PubMed] [Google Scholar]
  48. McReynolds L. S., , Schwalbe C. S., , & Wasserman G. A. (2010). The contribution of psychiatric disorder to juvenile recidivism. Criminal Justice and Behavior, 37(2), 204–216. 10.1177/0093854809354961 [Google Scholar]
  49. Mechanic D., , Angel R., , & Davies L. (1991). Risk and selection processes between the general and the specialty mental health sectors. Journal of Health and Social Behavior, 32(1), 49–64. 10.2307/2136799 [PubMed] [Google Scholar]
  50. Mertz E., , Wides C., , & White J. (2017). Clinician attitudes, skills, motivations and experience following the implementation of clinical decision support tools in a large dental practice. The Journal of Evidence-Based Dental Practice, 17(1), 1–12. 10.1016/j.jebdp.2016.10.001 [DOI] [PubMed] [Google Scholar]
  51. Nelson V., , Wood J., , Belenko S., , Pankow J., , & Piper K. (2024). Conditions of successful treatment referral practices with justice-involved youth: Qualitative insights from probation and service provider staff involved in JJ-TRIALS. Journal of Substance Use and Addiction Treatment, 162, 209358. 10.1016/j.josat.2024.209358 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Orthner D. K., , Cook P., , Sabah Y., , & Rosenfeld J. (2006). Organizational learning: A cross-national pilot-test of effectiveness in children’s services. Evaluation and Program Planning, 29(1), 70–78. 10.1016/j.evalprogplan.2005.08.002 [Google Scholar]
  53. Pankow J., , Joe G. W., , Robertson A. A., , Gardner S. K., , McReynolds L. S., , Dickson M. F., , Bartkowski J. P., , Arrigona N., , Johansson P., , Joseph E. D., , Krupka K. E., , Sease T. B., , & Knight D. K. (2024). Effects of substance use treatment on recidivism for youth in need of treatment. Journal of Criminal Justice, 95, 102304. 10.1016/j.jcrimjus.2024.102304 [Google Scholar]
  54. Pedhazur E. J. (1997) Multiple regression in behavioral research: Explanation and prediction. Harcourt Brace. [Google Scholar]
  55. Pullmann M. D., , Kerbs J., , Koroloff N., , Veach-White E., , Gaylor R., , & Sieler D. (2006). Juvenile offenders with mental health needs: Reducing recidivism using wraparound. Crime & Delinquency, 52(3), 375–397. 10.1177/0011128705278632 [Google Scholar]
  56. Puzzanchera C. M., , Hockenberry S., , & Sickmund M. (2022. Youth and the juvenile justice system: 2022 national report. National Center for Juvenile Justice. https://www.ncjfcj.org/wp-content/uploads/2023/01/2022-national-report.pdf [Google Scholar]
  57. Rainer T., , Lim J. K., , He Y., , Perdomo J., , Nash K. A., , Kistin C. J., , Tolliver D. G., , McIntyre E., , & Hsu H. E. (2023). Structural racism in behavioral health presentation and management. Hospital Pediatrics, 13(5), 461–470. 10.1542/hpeds.2023-007133 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rawal P., , Romansky J., , Jenuwine M., , & Lyons J. (2004). Racial differences in the mental health needs and service utilization of youth in the juvenile justice system. The Journal of Behavioral Health Services & Research, 31(3), 242–254. 10.1097/00075484-200407000-00002 [DOI] [PubMed] [Google Scholar]
  59. Revelle W. (2022. psych: Procedures for personality and psychological research. Northwestern University. https://CRAN.R-project.org/package=psych [Google Scholar]
  60. Rollnick S., , & Miller W. R. (1995). What is motivational interviewing? Behavioural and Cognitive Psychotherapy, 23(4), 325–334. 10.1017/S135246580001643X [DOI] [PubMed] [Google Scholar]
  61. Ryan M. E., , Elkington K. S., , Sarapas C., , Sichel C., , Taxman F., , Dennis M. L., , & Wasserman G. A. (2023). Evaluating e-Connect: Mitigating race by gender disparities in behavioral health referral among youths on probation, northeast United States, 2019. American Journal of Public Health, 113(12), 1267–1270. 10.2105/AJPH.2023.307417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Sarapas, C., Sichel, C. E., Dennis, M. L., Wasserman, G. A., Taxman, F. S., Auerbach, R. P., Mroczkowski, M. M., Ryan, M. E., & Elkington, K. S. (2025). Predictive Validity of the e-Connect Suicide Risk Classification Algorithm in Youth on Probation. JAACAP Open [advance online publication]. 10.1016/j.jaacop.2025.04.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Schubert C. A., , Mulvey E. P., , & Glasheen C. (2011). Influence of mental health and substance use problems and criminogenic risk on outcomes in serious juvenile offenders. Journal of the American Academy of Child and Adolescent Psychiatry, 50(9), 925–937. 10.1016/j.jaac.2011.06.006 [DOI] [PubMed] [Google Scholar]
  64. Scott S. G., , & Bruce R. A. (1994). Determinants of innovative behavior: A path model of individual innovation in the workplace. Academy of Management Journal, 37(3), 580–607. 10.5465/256701 [Google Scholar]
  65. Shook J. J., , Vaughn M. G., , & Salas-Wright C. P. (2013). Exploring the variation in drug selling among adolescents in the United States. Journal of Criminal Justice, 41(6), 365–374. 10.1016/j.jcrimjus.2013.07.008 [Google Scholar]
  66. Skeem J. L., , Louden J. E., , Polaschek D., , Camp J., , & Strauss M. E. (2007). Assessing relationship quality in mandated community treatment: Blending care with control. Psychological Assessment, 19(4), 397–410. 10.1037/1040-3590.19.4.397 [DOI] [PubMed] [Google Scholar]
  67. Steiner B., , Travis L. F., , Makarios M. D., , & Brickley T. (2011). The influence of parole officers’ attitudes on supervision practices. Justice Quarterly, 28(6), 903–927. 10.1080/07418825.2010.539246 [Google Scholar]
  68. Stiffman A. R., , Pescosolido B., , & Cabassa L. J. (2004). Building a model to understand youth service access: The gateway provider model. Mental Health Services Research, 6(4), 189–198. 10.1023/B:MHSR.0000044745.09952.33 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Sutton R. T., , Pincock D., , Baumgart D. C., , Sadowski D. C., , Fedorak R. N., , & Kroeker K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17–10. 10.1038/s41746-020-0221-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Taxman F. S., , Young D. W., , Wiersema B., , Rhodes A., , Mitchell S. (2007). The National Criminal Justice Treatment Practices Survey: Multilevel Survey methods and procedures. Journal of Substance Abuse Treatment, 32(3), 225–238. 10.1016/j.jsat.2007.01.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Taxman, F. S., Henderson, C., Young, D., & Farrell, J. (2012). The impact of training interventions on organizational readiness to support innovations in Juvenile Justice Offices. Administration and Policy in Mental Health and Mental Health Services Research, 41(2), 177–188. 10.1007/s10488-012-0445-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Teplin L. A., , Abram K. M., , McClelland G. M., , Washburn J. J., , & Pikus A. K. (2005). Detecting mental disorder in juvenile detainees: Who receives services. American Journal of Public Health, 95(10), 1773–1780. 10.2105/AJPH.2005.067819 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Teplin L. A., , Welty L. J., , Abram K. M., , Dulcan M. K., , & Washburn J. J. (2012). Prevalence and persistence of psychiatric disorders in youth after detention: A prospective longitudinal study. Archives of General Psychiatry, 69(10), 1031–1043. 10.1001/archgenpsychiatry.2011.2062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Toth-Pal E., , Wårdh I., , Strender L. E., , & Nilsson G. (2008). A guideline-based computerised decision support system (CDSS) to influence general practitioners management of chronic heart failure. Informatics in Primary Care, 16(1), 29–39. 10.14236/jhi.v16i1.672 [DOI] [PubMed] [Google Scholar]
  75. Wasserman G. A., , McReynolds L. S., , Musabegovic H., , Whited A. L., , Keating J. M., , & Huo Y. (2009). Evaluating Project Connect: Improving juvenile probationers’ mental health and substance use service access. Administration and Policy in Mental Health, 36(6), 393–405. 10.1007/s10488-009-0229-8 [DOI] [PubMed] [Google Scholar]
  76. Wasserman G. A., , McReynolds L. S., , Taxman F. S., , Belenko S., , Elkington K. S., , Robertson A. A., , Dennis M. L., , Knight D. K., , Knudsen H. K., , Dembo R., , Ciarleglio A., , & Wiley T. R. A. (2021). The missing link(age): Multilevel contributors to service uptake failure among youths on community justice supervision. Psychiatric Services, 72(5), 546–554. 10.1176/appi.ps.202000163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wasserman G. A., , McReynolds L. S., , Whited A. L., , Keating J. M., , Musabegovic H., , & Huo Y. (2008). Juvenile probation officers’ mental health decision making. Administration and Policy in Mental Health and Mental Health Services Research, 35(6), 477–478. 10.1007/s10488-008-0191-x [DOI] [PubMed] [Google Scholar]
  78. Woltmann E. M., , Whitley R., , McHugo G. J., , Brunette M., , Torrey W. C., , Coots L., , Lynde D., , & Drake R. E. (2008). The role of staff turnover in the implementation of evidence-based practices in mental health care. Psychiatric Services, 59(7), 732–737. 10.1176/ps.2008.59.7.732 [DOI] [PubMed] [Google Scholar]
  79. Zeola M. P., , Guina J., , & Nahhas R. W. (2017). Mental health referrals reduce recidivism in first-time juvenile offenders, but how do we determine who is referred? The Psychiatric Quarterly, 88(1), 167–183. 10.1007/s11126-016-9445-z [DOI] [PubMed] [Google Scholar]
  80. Zimmermann E., , Sample J. M., , Zimmermann M. E., , Sullivan F., , Stankiewicz S., , & Saldinger P. (2018). Successful implementation of an alcohol screening, brief intervention, and referral to treatment program. Journal of Trauma Nursing, 25(3), 196–200. 10.1097/JTN.0000000000000368 [DOI] [PubMed] [Google Scholar]

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