Size and Attributes of Probation Officers’ Service Provider Networks: Connecting Individuals with Mental Illnesses to Community-based Supports

Abstract

This exploratory study examines inter-organizational communication patterns and information sharing between probation officers and service providers when coordinating services for people with mental illnesses on probation. Thirty-four probation officers from one rural (n=12) and one urban (n=22) county completed a researcher-administered questionnaire pertaining to the size of probation officers’ service provider networks and the frequency and nature of contacts with those networks. Egocentric network analysis and bivariate inferential statistics were used to examine direct relational ties between each officer and the service providers within their communities. Probation officers in both counties reported high frequency of contact with service providers and indicated that service providers and officers were more likely to share information if reciprocated by their dyad counterpart. Probation agencies may consider enhancing probation officers’ service provider networks and fostering reciprocal and mutually-beneficial relationships to ensure timely access to services for adults with mental illnesses who are on probation.

The estimated 800,000 to 1.2 million people on probation who have a mental illness (Crilly et al., 2009; Ditton, 1999; Kaeble, 2018) pose significant supervision challenges due to characteristics of the individual on probation (e.g., challenges with substance use, lack of income), probation officer and workforce capacity (e.g., insufficient training, time constraints, large caseload sizes), and community resource availability (e.g., scarcity of treatment options, under- or unemployment, housing instability: Sirdifield and Owen, 2016; Van Deinse et al., 2018). To address the specific needs of individuals with mental illnesses on probation and related supervision challenges, probation agencies employ “thick supervision” approaches that enhance collaboration and coordination between probation, behavioral health and social service providers (Dominey, 2019).

Specifically, in the context of thick supervision, officers supervising high-need clients are expected to increase contacts and coordination with community-based treatment providers with the shared goal of helping individuals integrate with their communities, access health and behavioral health services, and establish a strong network of prosocial supports (Dominey, 2019). Successful implementation of ‘thick supervision’ and interventions that involve referrals to community-based treatments rely on enhanced coordination and collaboration, which is contingent upon the robustness of the local provider network and inter-organizational relationships between probation officers and service providers (Monico et al., 2016).

For example, in a scoping review examining the barriers and facilitators to treatment implementation, specifically medications for opioid use disorder (MOUD) in the criminal justice system, coordination, communication, and inter-organizational relationships between corrections and community services or treatment providers facilitated MOUD implementation (Grella et al., 2020). In addition, in a probation-based study of an organizational linkage intervention, researchers targeted the quality of inter-organizational relationships in order to improve access to medication-assisted treatment (MAT; Friedmann et al., 2013; Friedmann et al., 2015; Welsh et al., 2016).

Robustness of these inter-organizational relationships is impacted by a number of factors. For example, probation officers’ ability to network with resource providers is fundamentally dependent upon the existence of resources in a given context. Resource availability, particularly mental health and other social services, varies by location and is often more limited in rural areas (Bird et al., 2001; Fiske et al., 2005; Reschovsky & Staiti, 2005). Indeed, lack of transportation, too few behavioral health providers covering large geographical catchment areas, and scarcity of housing and other services, such as supported employment (Probst et al., 2019; Wolfe et al., 2020), are common among rural areas and may make it difficult for probation officers who are trying to connect individuals with a variety of services and supports.

Further, communication and collaboration between probation officers and social service providers may be complicated by the differences in the primary goals and objectives of the respective agencies and disciplines. Whereas mental health service providers’ primary goal is the treatment and rehabilitation of an individual, the probation officer’s responsibility is to protect public safety and enforce the terms of supervision. These different foci could potentially lead to complications with officers and agency personnel working together to identify a common goal and acceptable course of action to achieve it, thus ultimately impacting the degree to which officers and service providers are networked (Monico et al., 2016).

Moreover, the extent to which probation officers communicate with one or only a few preferred service providers versus working with and communicating with many providers is unclear. It could be the case that probation officers work most closely with those providers who reciprocate information sharing, make themselves available, return phone calls, and do their part to establish a good working relationship with the probation officer; thus, probation officers may identify one or two providers that become their main source of support for all of the individuals on their caseloads who need services. This is speculative, however, and more research is needed to understand the size of probation officers’ provider networks and the information sharing which occurs within these networks, especially given how probation officers now play a significant role in connecting individuals on their caseloads who have behavioral health needs to community resources.

There is little empirical research about the extent to which probation officers communicate with community-based social service providers, however, and limited information about how that communication is reciprocated. This is a significant gap in the literature, especially in the context of interventions, such as specialty mental health probation, that necessitate frequent and effective collaboration among multiple systems. Given the importance of inter-organizational relationships between probation officers and social service providers, as well as the variation in resource availability across local contexts, understanding the variation in probation officer-provider networks across urban and rural settings is important and informative to policy and practice for both probation and social service agencies.

Here to address this topic, we report findings from an exploratory study of probation officers’ networks in one urban and one rural county and address the following research objectives: (1) describe the service provider networks of probation officers with respect to network size and agency type; (2) describe communication patterns and information sharing between probation officers and service providers; and (3) compare the characteristics of probation officers’ service provider networks between rural and urban settings.

Materials and Methods

Design

This is an exploratory, cross-sectional study that examines relationships between probation officers and service providers in two counties – one urban and one rural – in a southeastern state. The research team collected data about the number of service provider contacts probation officers had, as well as the frequency and nature of probation officer communication with service providers. Data collection took place in July 2014. All study procedures were approved by the Institutional Review Board at the University of North Carolina at Chapel Hill.

Sample

To select a representative sample of probation officers from the large urban county, researchers stratified a list of all 114 probation officers in the county by unit and randomly selected two probation officers from each of the 15 units (n =30). When a probation officer declined to participate (n = 1) or did not respond (n = 8), researchers randomly selected another officer from the same unit. Of the 30 probation officers selected to participate in the urban county, five were excluded due to scheduling conflicts on the day of the interview and three were excluded because they were not currently supervising a caseload, resulting in 22 probation officers from the urban county. In the rural county, researchers invited all 16 probation officers to participate and 15 completed the questionnaire with a research team member and one was unable to attend either of the two data collection days. An additional 3 probation officers were excluded due to non-identification of service provider data, which brought the final sample to 34 probation officers (22 probation officers from the urban county and 12 officers from the rural county).

Measures

Data were collected using a researcher-created social network analysis questionnaire. First, respondents were asked about basic demographics, their years of experience as a probation officer, their current caseload size, and to estimate the number of individuals with mental illnesses on their caseloads. The research team asked the probation officers to estimate the number of people with mental illnesses on their caseloads for three reasons. First, probation officers supervising standard caseloads in this state do not have access to confirmed clinical diagnoses. Second, although the state’s risk needs assessment does have questions pertaining to mental health and psychiatric medication use, probation officers did not have access to the system as they were completing the questionnaire with the research team members. Third, despite limitations in prevalence estimates, it was important to determine whether the probation officers had experience supervising people with confirmed or suspected mental illnesses so that they could contribute meaningfully to the study.

Next, to identify patterns and pathways of inter-organizational communication (Aarons et al., 2011) between probation officers and service providers, participants were asked to think about individuals currently on their probation caseloads for whom they had to coordinate mental health services and to consider the other service and resource needs of those individuals. Then, probation officers were asked to identify specific service providers to which they refer clients or connect with while supervising those with mental illnesses on their caseload. For each service provider, officers answered the following items:

1. the type of service provider (e.g., mental health service provider, substance use service provider, social services, etc.)

2. the number of staff members at the service provider with which the probation officers were in contact

3. whether the probation officers shared progress reports, compliance information, and service connection information regarding individuals on their caseloads

4. how often the probation officers shared information with the service provider (i.e., couple of times a week, once a week, once a month, once every two months, or “I do not share information with this agency”)

5. how often the service provider shared information with the probation officer (i.e., couple of times a week, once a week, once a month, once every two months, or “The organization does not share information with me about the offenders with mental illness on my caseload”)

6. how often the probation officer sought advice and collaboration from the service provider (i.e., couple of times a week, once a week, once a month, once every two months, or “I do not seek advice or guidance from this organization about the offenders with mental illness on my caseload”)

7. how often the service provider sought advice from the probation officer (i.e., couple of times a week, once a week, once a month, once every two months, or “The agency/organization does not seek advice about working with offenders with mental illness on my caseload”)

8. how often the probation officer and service provider worked together to solve problems facing the individual (i.e., couple of times a week, once a week, once a month, once every two months, or “I do not work together with the organization to solve problems or address issues with the offenders with mental illness on my caseload”)

These questions were meant to characterize and describe the relationships between the officers and each of the service providers named.

Data Analysis

Social network analysis (SNA) methods were used to examine characteristics of probation officers’ service provider networks. SNA is a unique method of mapping and assessing relationships between people or groups of people known as actors (e.g., individuals, families, organizations; Wasserman & Faust, 1994). The key unit of study in SNA is the network of relations between actors and the structures, or communication patterns, that are established through these relationships.

In the current study, we used egocentric network analysis (i.e., personal network analysis), a type of SNA, to examine direct relational ties between each probation officer (i.e., ego, the individual who is the focal node) and the service providers within their respective communities (i.e., alters, the contacts listed by the probation officer or ego) (Rice & Barman-Adhikari, 2015; Rice & Yoshioka-Maxwell, 2015). The two primary variables examined in the probation officer networks are out-degree (Hanneman & Riddle, 2005; Scott, 2012), which refers to the number of ties or referrals that originate from a given probation officer. This number is obtained by counting the number of service providers to which a given probation officer refers or otherwise connects with. The second variable is the in-degree of a given service provider (Hanneman & Riddle, 2005; Scott, 2012). Here, in-degree refers to the number of times that probation officers named a given service provider that they refer to or otherwise connect with while supervising those on their caseload with mental illnesses.

We were also interested in examining patterns of communication and advice seeking between probation officers and service providers, also known as dyads. Analyses of the resulting two-mode networks (i.e., ties between probation officers and different service providers) included bipartite graphs to aid network visualization and comparison. We used chi-square tests for categorical measures used to describe and compare the patterns of communication and advice-seeking between dyads. The data for these analyses were obtained from questions 1 through 8 above.

A sample illustration of two probation officers’ ego networks (i.e., the network of organizations officers are connected to with respect to supervising people with mental illnesses), is provided in Figure 1. On the left side of the diagram, Officer 1 identified one service provider contact with an organization that provides substance use services. On the right side, Officer 2 identified four service provider contacts with a social services provider, a housing services provider, a substance use services provider, and a mental health services provider. In this example, Officer 1 has an out-degree of 1 and Officer 2 has an out-degree of 4, indicating that Officer 2 connects with a larger network of service providers than Officer 1 when supervising a person with mental illnesses.

Figure 1.

Sample Ego Networks

Figure 2 illustrates the concept of in-degree using a sample bipartite graph. On the top part of the graph, officer out-degree varies from 1 (Officers 1 and 4) to 4 (Officer 2), meaning that Officers 1 and 4 are connected with one service provider and Officer 2 is connected with 4 service providers. On the bottom part of the graph, there are 4 service providers identified by probation officers. The in-degree of service providers (i.e., the number of ties originating from probation officers) ranges from 1 (SP 2) to 3 (SP 4), indicating that service provider 4 (SP 4) likely receives more referrals or other contacts from probation officers in that county.

Figure 2. Sample Bipartite Graph.

Note. SP = service provider.

Network analyses, including bipartite graphs, were conducted using R (R Core Team, 2017). Chi square tests and t-tests were conducted using Stata 16 (StataCorp, 2016).

Results

To describe probation officers’ service provider networks, we first present findings from the ego network analysis and examine the in-degree of the service providers (i.e., the number of probation officers indicating that they referred to the service provider), the out-degree of the probation officers (i.e., the number of service providers to which officers refer individuals), and then the patterns of communication and contact between probation officer-service provider dyads.

Study Sample

Table 1 displays the probation officer characteristics by urban and rural setting. Of the 34 probation officers in the sample, 58.82% (n = 20) identified as female, 47.06% (n = 16) were White, 50.00% (n = 17) were Black or African American, 2.94% (n = 1) were American Indian or Alaskan Native, and 88.24% (n = 30) had a bachelor’s degree or graduate-level education. The average age of the probation officers was 40.56 years (SD = 10.59) and they had worked in their current position as community supervision officers for an average of 7.50 years (SD = 6.06) and worked at their current probation agency, on average, for 8.90 years (SD = 7.10). Probation officers’ average caseload size was 64.62 supervisees (SD = 23.17) with an estimated 13.58 (SD = 9.43) individuals with mental illnesses per caseload, or approximately 21%.

Table 1.

Probation Officer Characteristics

	Total(n=34)	Urban (n=22)	Rural (n=12)
Age M(SD)	40.56 (10.59)	41.86 (9.37)	38.17 (12.63)
Gender %(n)
Male	41.18 (14)	45.45 (10)	33.33 (4)
Female	58.82 (20)	54.55 (12)	66.67 (8)
Hispanic/Latino	0	0	0
Race % (n)
White	47.06 (16)	40.91 (9)	58.33 (7)
Black or African American	50.00 (17)	59.09 (13)	33.33 (4)
American Indian or Alaskan Native	2.94 (1)	0	8.33 (1)
Education level %(n)
High school diploma	3.03 (1)	4.55 (1)	0
Associate’s degree	6.06 (2)	9.09 (2)	0
Bachelor’s degree	57.58 (19)	40.91 (9)	90.91 (10)
Some graduate courses	9.09 (3)	9.09 (2)	9.09 (1)
Master’s degree	24.24 (8)	36.36 (8)	0
Years in position M(SD)	7.50 (6.06)	7.57 (4.85)	7. 38 (8.07)
Years at agency M(SD)	8.90 (7.10)	9.09 (6.70)	8.54 (8.08)
Caseload size M(SD)	64.62 (23.17)	65.14 (25.09)	63.67 (20.16)
Est. caseload w/MI M(SD)	13.58 (9.43)	14.4 (8.93)	12.09 (10.56)

Comparing Probation Officer Networks

Fifty-five unique service providers were identified across the rural and the urban counties, 39 of which were identified in the urban county and 16 in the rural county. Service provider in-degree, measured here by the number of ties originating from the probation officers (i.e., probation officers indicating that they referred to a given service provider), ranged from 1 to 16 in the urban county and 1 to 11 in the rural county. In both counties, referrals to service providers clustered around a few key organizations with an in-degree of 6 or more, meaning that at least six probation officers in that county reported referring to a specific service provider. In addition, the urban county had a higher percentage of peripheral service providers (i.e., peripheral providers were those that were named by only one probation officer; 56%; n = 22) compared to those in the rural county (43%; n = 7; see Figures 3 and 4).

Figure 3. Bipartite Graph of Urban Probation Officers’ Identified Service Providers.

Note. Probation officers are represented by squares, and service providers by circles.

Figure 4. Bipartite Graph of Rural Probation Officers’ Identified Service Providers.

Note. Probation officers are represented by squares, and service providers by circles.

The out-degree, or the number of providers with which probation officers connect while supervising individuals with mental illnesses, ranged from 1 to 11 in the rural county and 2 to 8 in the urban county. The median out-degree was comparable in both counties, with a median of 4.5 in the rural county and 5 in the urban county, indicating that size of the probation officers’ service provider networks in the rural and urban counties were about the same.

Examining Patterns of Contact between Probation Officers and Service Providers

Patterns of communication were examined between each of the 164 provider-officer dyads (i.e., unique connections between probation officers and service providers), 108 (66%) of which were urban dyads and 56 (34%) were rural dyads. In terms of information sharing, in 74% (n = 122) of dyads, probation officers indicated that they shared information with providers. Officer information sharing was higher among urban dyads (80%, n = 86) compared to rural dyads (64%, n = 36; p<.05). In a third of the dyads (33%, n = 54), probation officers shared information once a month and 20% (n = 34) shared information once every couple of months.

In addition, in 73% (n = 119) of the dyads, probation officers reported that providers shared information with them and this finding was comparable across urban and rural dyads. Further, the proportion of dyads in which providers shared information was higher when officers also shared information with providers (89%, n = 109) compared to dyads in which officers did not share information (24%, n = 10; p<.001). For dyads in which information sharing was reported, in nearly a third of the dyads (30%, n = 49) providers shared information with officers on a monthly basis and in 19% (n = 31) of the dyads providers shared information with officers every couple of months.

In terms of advice-seeking, officers were more likely to report seeking advice from providers than providers seeking advice from officers. In 52% (n = 86) of the dyads, officers indicated that they sought advice from service providers compared to 34% (n = 56) of the dyads in which providers sought advice from probation officers (p<0.001). Further, the proportion of dyads in which providers sought advice from officers was higher when probation officers sought advice from providers (88%, n = 49) compared to dyads in which officers did not seek advice from providers (34%, n = 37; p<.001). In nearly half of all dyads (49%, n = 80), probation officers reported that they collaborated with providers to solve problems and this was comparable between the urban (52%; n = 56) and rural (43%, n = 24) dyads.

Although the frequency and pattern of communication were consistent across the urban and rural counties, there were differences in the nature of the contact between probation officers and providers. In the urban county, 58% (n = 63) of the dyads communicated to conduct progress reports compared to 34% (n = 19) of the rural dyads. In addition, 69% (n = 75) of the urban dyads compared to 48% (n = 27) of the rural dyads were in contact in order to connect the individual to services. Further, in 94% (n = 101) of the urban dyads multiple reasons for contact were identified (e.g., service connection, progress reports) compared to 82% (n = 46) of the rural dyads.

Discussion

This exploratory study assessed probation officers’ networks of resource providers (e.g., behavioral health services) with whom they coordinated while supervising individuals with mental illnesses, and compared these networks across rural and urban counties. Findings suggested that probation officers are connected with a variety of service provider agencies when supervising individuals with mental illnesses, including outpatient mental health and substance use service providers, psychiatric hospitals, behavioral health crisis services, employment services, homeless shelters, intimate partner and domestic violence programs, and other law enforcement agencies. In addition, probation officers reported high rates of frequent (i.e., at least monthly) contact and information sharing with service providers. The diversity in service provider networks and frequency of communication with those networks suggests that probation officers develop a breadth of knowledge about service availability and accessibility in order to manage their caseloads, particularly when working with those with mental illnesses.

Findings also suggest that, despite having more service providers and types of services in the urban county compared with the rural county, there was little variation in the median size (i.e., out-degree) of the probation officers’ service provider networks. This finding suggests that service provider availability and density might not necessarily impact the size of probation officer networks. Rather, other factors may influence the number of service providers with which officers connect when supervising someone, especially when supervising those with mental illnesses.

For instance, probation officers may establish reliable contacts with only one or two service providers and routinely turn to those providers for referral and service connection. In addition, probation officers’ other supervision obligations and existing workload may limit their capacity for seeking out new partnerships with service providers, resulting in a natural limit on the number of service providers in their networks. It is possible that probation officers prioritize individuals’ needs and focus on coordinating and collaborating with service providers who are able to help officers with meeting the most pressing needs of those on their caseloads (e.g., mental health services).

Further, service providers and probation officers were more likely to share information or seek advice if their counterpart also did (Table 3). That is, probation officers who reported that they communicated and shared information with their service provider counterpart in a given dyad were more likely to indicate that the service provider also shared information and communicated regularly. These reciprocal relationships between probation officers and service providers may indicate a shared sense of importance of establishing bidirectional collaboration and coordination between officers and service providers in order to address the needs and challenges of individuals with mental illnesses on probation caseloads.

Table 3.

Two-Way Patterns of Communication between Probation Officer and Provider Dyads

		Officer		P	X2
	Provider	Yes	No
Advice is offered	Yes	49 (87.50)	37 (34.26)	<.001	41.9
	No	7 (12.50)	71 (65.74)
Information is shared	Yes	109 (89.34)	10 (23.81)	<.001	67.4
	No	13 (10.66)	32 (76.19)

Limitations

The findings in this exploratory study should be considered in light of several limitations. First, the sample included a small number of probation officers from two counties representing an urban and rural setting in one state, thus limiting the generalizability of these findings. Second, the data collection process required probation officers to recollect their experiences of all service providers they had interacted with within the context of supervising individuals with mental illnesses currently on their caseloads. Probation officers may have over- or under-reported interactions due to general recall error. In addition, though not collected for this study, information pertaining to job performance and workload of probation officers would be helpful in understanding study findings. It is likely that the size of officers’ resource network may be correlated with other factors related to job performance and capacity.

Third, data were only collected from officers and not from service providers. It is likely that service providers would provide additional relevant information that would help inform the relationships between officers and service providers. Fourth, the patterns of communication (e.g., frequency and nature) between probation officers and service providers are nested, such that one probation officer can indicate multiple service providers in the network. Consequently, data from a probation officer that has frequent contact with multiple agencies may result in over-representation of high frequency of contacts with service providers.

Finally, the behavioral health service providers all operated under a managed care framework in which services are authorized by a local management entity. Consequently, this type of managed care approach likely impacts the number and type of services in a given community, thus the extent to which our findings can be generalized to other areas with different behavioral health service financing and delivery models (e.g., communities that may have a single behavioral health service provider for the county) is limited. Nevertheless, this study provides a unique contribution to the literature about the relationships between probation officers and service provider networks.

Implications

Over the course of their duties, probation officers are tasked with enforcing the terms of community supervision, ensuring public safety, and may be expected to connect individuals with mental illnesses to necessary services and supports, such as housing, behavioral health treatment, and employment resources. Probation agencies face administrative challenges due to: (a) the complex needs of individuals with mental illnesses, (b) relatively high prevalence of mental health challenges on probation caseloads, and (c) the competing demands on probation officers.

Consequently, some probation agencies may consider implementing a mental health supervision approach, such as specialty mental health probation (i.e., mental health caseloads). With mental health probation approaches, probation administrators can designate specialized mental health caseloads supervised by specially trained probation officers. These caseloads are typically reduced in size (Manchak et al., 2014; Skeem et al., 2006; Skeem et al., 2017; Manchak and Montoya, 2017; Van Deinse et al., 2021; Wolff et al., 2014) and are either mixed (i.e., comprised of individuals with mental illnesses and those without) or designated (i.e., only for individuals on probation who have a mental illnesses). With a reduction in caseload size, specialty mental health probation officers may be able to establish more robust service provider networks with frequent communication to ensure treatment access and probation compliance.

Regardless of whether agencies decide to implement a mental health probation approach, probation agencies can develop strategies for enhancing officers’ resource networks to link individuals with mental illnesses to community-based services. Specifically, developing information sharing protocols, conducting cross-training, fostering collaborative problem-solving between mental health service providers and probation officers, and other strategies for addressing communication barriers can enhance officer networks and build effective communication patterns. Given the study findings showing that probation officers and service providers were more likely to share expertise and advice if there was reciprocity in sharing, establishing protocols and expectations for consistent and timely communication between probation officers and providers could be fundamental to creating reciprocal and mutually beneficial relationships that can help ensure timely access to necessary services and supports for adults with mental illnesses.

In addition, probation agencies may seek cross-training opportunities in which probation departments educate service providers about probation supervision, court requirements and the importance of timely communication and coordination with probation officers. Likewise, service providers may train probation officers in the types of services offered as well as strategies to engage individuals and ensure compliance. This cross-training reinforces the reciprocal relationship between service providers and probation officers indicated in the study findings.

Further, although findings from this study do not address the ideal size of an officer network, it is reasonable to assume that a minimum degree of one or two service provider agencies may not be sufficient for service coordination and that expanding officers’ networks may provide additional opportunities for individuals on an officer’s caseload. In this study, the maximum range in probation officer degree (i.e., size of the officers’ network) was 1 to 11 indicating a wide variation among officers and the potential for using the bridging capital of officers with higher-degree networks. Assuming variation in the degree or size of probation officer networks in a given unit, officers with lower-degree networks can leverage the relationships between officers with higher-degree networks to form new ties. In other words, probation officers who are connected with a greater number of service provider agencies can help other probation officers establish contacts to enhance the size of their networks and increase their ability to connect individuals on their caseload with necessary community-based supports.

It is important to note that although these pathways for bridging ties may exist, the capacity of probation officers to facilitate those ties may be limited by existing workload demands. In this context, approaches that bridge referrals and coordination with behavioral health services (e.g., Treatment Accountability for Safer Communities) may help address this issue. In addition, agencies may consider dedicated positions designed to establish and formalize the relationships between probation officers and service providers (Sirdifield & Owen, 2016).

Conclusion

Due to the large number of individuals with mental illnesses under community supervision, probation officers interface with a number of different behavioral health service providers and other resources as they enforce the terms of supervision and connect individuals to necessary services and supports. Despite geographic (e.g., rural and urban) variation in the number of available service providers, the size of probation officers’ service provider networks may be consistent suggesting limited capacity for connecting with additional service providers, the existence of routine patterns of collaboration and coordination, or prioritization of service needs. Further, establishing protocol and expectations for consistent and timely communication between service providers and probation officers is fundamental to creating reciprocal and mutually beneficial relationships that can help ensure timely access to necessary services and supports for adults with mental illnesses.

Table 2.

Patterns of Communication between Probation Officer and Provider Dyads

	Total (n=164) %(n)	Urban County (n=108) %(n)	Rural County (n=56) %(n)
Nature of contact and information sharing with providers
Provide progress reports**	50.00 (82)	58.33 (63)	33.93 (19)
Connect to services**	62.20 (102)	69.44 (75)	48.21 (27)
Ensuring compliance	61.59 (101)	61.11 (66)	62.50 (35)
Other**	12.20 (20)	17.59 (19)	1.79 (1)
Does not contact*	10.37 (17)	6.48 (7)	17.86 (10)
Multiple reasons*	89.63 (147)	93.52 (101)	82.14 (46)
Officer shares information with providers *	74.39 (122)	79.63 (86)	64.29 (36)
Frequency of sharing information with providers
A couple times a week	9.15 (15)	9.26 (10)	8.93 (5)
Once a week	11.59 (19)	10.19 (11)	14.29 (8)
Once a month	32.93 (54)	37.96 (41)	23.21 (13)
Once every couple of months	20.73 (34)	22.22 (24)	17.86 (10)
Does not share	25.61 (42)	20.37 (22)	35.71 (20)
Provider shares information with officers	72.56 (119)	75.00 (81)	67.86 (38)
Frequency of providers sharing information with officers
A couple times a week	8.54 (14)	6.48 (7)	12.50 (9)
Once a week	15.24 (25)	14.81 (16)	16.07 (9)
Once a month	29.88 (49)	31.48 (34)	26.79 (15)
Once every couple of months	18.90 (31)	22.22 (24)	12.50 (7)
Does not share	27.44 (45)	25.00 (27)	32.14 (18)
Officer seeks advice from provider	52.44 (86)	51.85 (56)	53.57 (30)
Frequency of officer seeking advice from provider
A couple times a week	2.44 (4)	2.78 (3)	1.79 (1)
Once a week	9.15 (15)	6.48 (7)	14.29 (8)
Once a month	23.78 (39)	25.00 (27)	21.43 (12)
Once every couple of months	17.07 (28)	17.59 (19)	16.07 (9)
Does not seek advice	47.56	48.15 (52)	46.43 (26)
Provider seeks advice from officer	34.15 (56)	37.04 (40)	28.57 (16)
Frequency of provider seeking advice from officer
A couple times a week	0	0	0
Once a week	9.15 (15)	8.33 (9)	10.71 (6)
Once a month	11.59 (19)	12.04 (13)	10.71 (6)
Once every couple of months	13.41 (22)	16.67 (18)	7.14 (4)
Does not seek advice	65.85 (108)	62.96 (68)	71.43 (40)
Provider and officer collaborate to problem-solve	48.78 (80)	51.85 (56)	42.86 (24)
Frequency of provider-officer collaboration to problem-solve
A couple times a week	1.83 (3)	2.78 (3)	0
Once a week	6.10 (10)	3.70 (4)	10.71 (6)
Once a month	25.61 (42)	30.56 (33)	16.07 (9)
Once every couple of months	15.24 (25)	14.81 (16)	16.07 (9)
Do not collaborate	51.22 (84)	48.15 (52)	57.14 (32)

^*p<.05,

^**p<.01

Biographies

Tonya Van Deinse, PhD, MSW

Tonya Van Deinse, PhD, MSW, is a clinical associate professor at the UNC Chapel Hill School of Social Work and a mental health services researcher focused on the development, implementation, and evaluation of interventions that span the mental health and criminal justice systems.

Ashley Givens, PhD, LCSW

Ashley Givens, PhD, LCSW is an assistant professor at the School of Social Work at the University of Missouri. Her research focuses on issues facing individuals involved in the criminal legal system, specifically traumatic histories, criminogenic risks and needs, and serious mental illnesses.

Joseph J. Frey, PhD, MSSW

Joseph J. Frey, PhD, MSSW, is an assistant professor in the Department of Social Work at the University of North Texas. Through his research, he explores the health and wellbeing of lesbian, gay, bisexual, transgender, and queer populations, as well as the functioning of social networks.

Mariah Cowell, MSW

Mariah Cowell, MSW, is a doctoral student at the University of Utah College of Social Work focusing on diversion from the criminal legal system, hyper-incarceration, and community-based strategies against structural violence.

Gary Cuddeback, PhD, MSW, MPH

Gary Cuddeback, PhD, MSW, MPH, is a professor and associate dean for research at the School of Social Work at Virginia Commonwealth University. Dr. Cuddeback’s research is focused on improving the lives of individuals with mental illnesses, especially those who are involved with the justice system.

Availability of data and material (data transparency)

Data from this study will be made available by the Principal Investigator (PI) as freely as possible while safeguarding the confidentiality of the data and privacy of participants. Researchers with a convincing scientific interest in the data can contact the study PI and establish a data-sharing agreement.