Sex Trafficking Prevention for Youth With Disabilities: Network Analysis of Organizational Coordination

Abstract

Purpose: Despite the elevated risk of youth with disabilities experiencing sex trafficking, little is known regarding coordination among organizations that provide services critical to prevention. Methods: This study utilized social network analysis to explore the association between six organizational sectors and three coordination types (i.e., referrals, information and resource sharing, and trainings). Organizational representatives (n = 47) were surveyed about their coordination, communication frequency, and trust and reliability toward 190 organizations in a southeastern state. Results: Communication frequency was significantly associated with all coordination types. Among disability and violence prevention organizations, 71.9% of information and resource sharing ties and 12.5% of training ties were present. Violence prevention organizations sent more referrals to disability organizations than the inverse. Discussion: Future research should explore how coalition building among organizations occurs via interpersonal ties (e.g., boundary spanners). Findings can influence cohesive responses to sex trafficking that incorporate service needs of youth with disabilities.

Keywords

disabilities social network analysis collaboration sex trafficking violence prevention

More than two decades since the passing of the Victims of Trafficking and Violence Protection Act (2000), a community-wide coordinated response to sex trafficking remains a priority in social work policy, research, and practice. Sex trafficking is defined as the receipt of persons (e.g., recruiting, harboring, transporting) via force, coercion, or fraud (e.g., false promises) for the purpose of commercial sexual exploitation (Trafficking Victims Protection Reauthorization Act, 2017). Despite challenges to estimating the true prevalence, cases of sex trafficking involving youth have been identified across the United States (Polaris Project, 2020). Sex trafficking is associated with numerous negative outcomes, including depression and anxiety, bodily injury, and sexually transmitted infections (Le et al., 2018; Levine, 2017; Muftić & Finn, 2013).

Research suggests that youth with socially marginalized identities (e.g., LGBTQ+ and racial/ethnic minority groups) are more likely to experience sex trafficking compared to those without marginalized identities (Gerassi et al., 2021; Harper, 2013). There is also an increased risk of sex trafficking experienced by youth with intellectual or developmental disabilities (IDD; Franchino-Olsen et al., 2020; Reid et al., 2018). Marginalized groups may be less likely to receive culturally responsive and inclusive prevention and response services (Kruger et al., 2013; Marburger & Pickover, 2020; Walker et al., 2022). This is complicated by the fact that preventing and responding to sex trafficking—particularly among youth with IDD—necessitates a collaborative approach involving multiple different organizational sectors. Using a social-ecological model to theoretically synthesize correlates of sex trafficking involving youth, Twis et al. (2025) note that environmental and societal factors (e.g., anti-trafficking awareness campaigns, interorganizational anti-trafficking training) are under-examined compared to individual-level factors (e.g., gender, family relationships). To further our understanding of the role of environment and societal factors in sex trafficking prevention, the current study explores coordination among organizations that would be required to work together to prevent and respond to sex trafficking among youth with IDD. In particular, this study focuses on aspects of coordination—that is, cross-sector referrals, information and resource sharing, training and educational opportunities, and inter-organizational trust—among organizations identified as key stakeholders.

Sex Trafficking Among Youth With IDD

Utilizing data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), Franchino-Olsen et al. (2022) found that boys with IDD were 2.80 times more likely to report exchanging sex for money or drugs than boys without IDD. Girls with IDD were 4.86 times more likely to trade sex than girls without IDD in another study analyzing Add Health data (Franchino-Olsen et al., 2020). Additionally, Carrellas et al. (2021) found that, among youth with prior child welfare involvement, youth who scored lower on the Kaufmann Brief Intelligence Test (K-BIT) had higher odds of exchanging sex for money. Youth with IDD who are victims of sexual abuse or sexual assault experience trauma and negative mental health (e.g., depression, anxiety) (Reid et al., 2018; Soylu et al., 2013). However, youth with IDD may present with different trauma symptoms than youth without IDD, notably self-injurious behavior, agitation and aggression, and noncompliance in activities (Mevissen & de Jongh, 2010; Reid et al., 2018). Given these nuances, it is important to consider the types of organizations that might be involved in preventing, identifying, or responding to sex trafficking among youth with IDD. For instance, indicators of sex trafficking appearing during routine medical evaluations merits the inclusion of healthcare organizations (Nazer, 2017; Scott, 2020). In order to access a myriad of specialized services (e.g., housing), youth with IDD who are victims of sex trafficking may require advocates who understand support needs related to both sexual victimization and disability, thus warranting the involvement of anti-violence and disability-serving organizations.

Organizational Coordination

Gulati et al. (2012) classify organizational coordination as an underlying facet of organizational collaboration, defining it as an alignment of organizations’ actions to accomplish the same goals. Coordination can ensure a fair division of labor and help manage uncertainties that arise from an external environment, with a recent example being the COVID-19 pandemic (Grizzle et al., 2020; Gulati et al., 2012; Puranam & Raveendran, 2013). In the context of a coordinated response to sex trafficking, a core feature is the involvement of multiple organizations within and across service sectors (Jones, 2023; Nixon-Cream, 2019). Specific coordination activities among these sectors include cross-sector referrals, sharing information and resources, and training or education opportunities. Additionally, inter-organizational trust is an important characteristic of sector networks that can impact the success of coordination (Todres, 2010).

Cross-Sector Referrals

In terms of prevention, youth receiving education on sex trafficking may disclose experiences of exploitation during or after learning about relevant terminology and resources (Chesworth et al., 2020). This might require referrals between schools, child protective services (CPS), and law enforcement agencies. In terms of response, individuals seeking services related to sex trafficking often experience immediate needs (e.g., access to emergency medical care, crisis shelters) and long-term needs (e.g., transitional housing, life skills training) (Macy & Johns, 2011). These efforts require coordinated referrals across multiple sectors, including medical healthcare, mental healthcare, and specialized services (e.g., vocational, housing).

Availability and accessibility of services influences the effectiveness of a referral network for sex trafficking survivors with IDD. A survey conducted by the National Human Trafficking and Disability Working Group found that 34% of disability service providers (n = 29) knew who to contact if they identified someone experiencing human trafficking, and 65% of anti-human trafficking professionals (n = 20) knew who to contact if a client had a disability (Bovat et al., 2022). Todres (2010) describes organizations’ “open channels of communication” and awareness of other entities’ work as essential to preventing vulnerable youth from failing to receive services (p. 46). As such, building referral networks should involve ongoing communication and documentation of common scenarios or issues encountered when handling disclosures, thereby supporting a coordinated response to sex trafficking (Nichols et al., 2023).

Sharing Information and Resources

Lack of information sharing across agencies presents as a challenge for collaboration and making referrals, particularly for determining trauma histories and maintaining client confidentiality (Fraser-Barbour, 2018). Information sharing is the inter-organizational exchange of knowledge or data and is a key component of coordination (Gulati et al., 2012; Kembro et al., 2014). Reluctance to share data or other pertinent information with organizations can lead to unnecessary duplication of services, thereby impacting allocation of resources (Colvin et al., 2021). Relatedly, resource sharing involves the distribution of material (e.g., building space), financial (e.g., funding), and personnel (e.g., staff) assets, often in response to service demand or cost reduction needs (Shah et al., 2016). Budget and time resources may be expended in order to acquire information (Yang & Maxwell, 2011). Negotiating resources in the form of clear agreements is particularly important; otherwise, organizational relationships can deteriorate and hinder future collaboration (Colvin et al., 2021). For complex societal problems, such as reducing sex trafficking vulnerability among youth with IDD, both information and resource sharing can lead to innovative solutions and generate better outcomes (Nezami et al., 2023).

Training and Education

Todres (2010) notes the importance of training and education in improving collaboration and service coordination among stakeholders. In terms of sex trafficking prevention and response for youth with IDD, this can range from general knowledge or awareness (e.g., warning indicators of sex trafficking) to clinical skills and specialized techniques (e.g., questioning techniques when youth with IDD disclose sex trafficking). For stakeholders to make referrals, it is important that they are knowledgeable about sex trafficking, can recognize and screen for sex trafficking among youth with IDD via formal questioning and assessment, can respond appropriately to disclosures, and are knowledgeable of community organizations and how to make referrals to these key stakeholders.

Trainings focused on increasing general knowledge and awareness about sex trafficking can improve self-efficacy in responding to sex trafficking among both child welfare workers (Harmon-Darrow et al., 2023) and healthcare professionals (Lo et al., 2022). Research suggests that disability service providers want training on human trafficking, and likewise, human trafficking professionals desire training on disabilities (Bovat et al., 2022). Equally important are specialized and applied trainings that go beyond knowledge and awareness, particularly for working with youth with IDD. A survey of mental health professionals demonstrated strong competence in disability awareness, but less competence in assessment and case conceptualization skills for working with clients with disabilities (Strike et al., 2004).

Cross-sector training for pre-service and in-service professionals can lead to further organizational collaboration and improve outcomes for service recipients. For example, Margolis et al. (2013) surveyed nutrition, public health, pediatric dentistry, social work, and disability program graduates about their attitudes and skills pertaining to cross-sector partnerships after completing interdisciplinary training education. The authors found that trained graduates reported more positive attitudes (e.g., value the contributions of other disciplines) and skills application (e.g., critically evaluate information from other disciplines) (Margolis et al., 2013). Additionally, cross-sector trainings have increased professionals’ knowledge and confidence in providing services to individuals with autism (Bono et al., 2022), community-dwelling older adults (Rowan et al., 2009), and adults experiencing homelessness accompanied by mental illness or justice involvement (Roy et al., 2023). Cross-sector training has been identified as a benefit of anti-sex trafficking coalition collaboration (Gerassi et al., 2017). However, cross-sector training and other forms of coordination that are characterized by tense or fractious relationships, namely mistrust, can lead to fragmentation.

Inter-Organizational Trust

Todres (2010) identifies mistrust among organization as an obstacle to a coordinated response to sex trafficking, particularly concerning competition for funding. While prior research has focused on mistrust between sex trafficking survivors and systems, such as law enforcement (Lavoie et al., 2019) and healthcare (Richie-Zavaleta et al., 2020), less is known regarding mistrust between systems or organizations that serve survivors of sex trafficking. Despite this, trustworthy relationships within and between organizational sectors are important to the success of anti-sex trafficking interagency collaboration (Jones, 2023; Nixon-Cream, 2019). Inter-organizational trust is considered integral to achieving outcomes in the organizational science literature (Zaheer et al., 1998) and can enhance coordination among competing nonprofit human service organizations (Bunger, 2013). In Valaitis et al.'s (2020) qualitative research on coordination between primary care and community-based services, inter-organizational trust was recommended to improve system navigation for older adults. Similarly, communication frequency and trust were the strongest predictors of organization coordination in a suicide prevention network (Menger et al., 2015). Trust is a key relational driver and precursor to effective collaboration, particularly knowledge sharing (Chen et al., 2014; Ouakouak & Ouedraogo, 2019). Therefore, enhancing trust may improve referrals, information and resource sharing, and training opportunities that inform collective action to prevent sex trafficking.

Current Study

There is currently limited research on how organizations relevant to preventing and responding to sex trafficking of youth with IDD engage in coordination. This study addresses that gap by providing a comprehensive understanding of inter-organizational dynamics which may influence macro-level social work practice, such as implementing anti-trafficking educational programs in schools or communities. One promising approach for examining organizational collaboration is social network analysis (SNA) (Gest et al., 2011). As Prell et al. (2009) note, SNA “can supplement qualitative information” by examining strong (or weak) ties among organizations and identifying prominent “actors” or nodes (e.g., agencies or community members who are well connected) (p. 506). This methodology is underutilized in social work and as a means to detect potential opportunities for collaborative violence prevention and response. Notable exceptions include Menger et al.'s (2015) work to strengthen suicide prevention in a Colorado community and Cook-Craig's (2010) analysis of capacity-building teams for statewide sexual violence prevention and response.

This study seeks to address the following research questions:
Which organizations have the most coordination (i.e., are in the core of the network) and which have the least coordination (i.e., are in the periphery of the network)?

Which organizations primarily engage in one-way coordination (e.g., only sending referrals, information sharing) and which engage in two-way coordination (e.g., both sending/receiving referrals)?

Is there an association between relationship strength (i.e., communication frequency, reliability, and trust) and types of coordination?

Method

Study methods were reviewed and approved by the Office of Human Research Ethics at the University of North Carolina at Chapel Hill (#23-0481). The study was guided by consultation with a community advisory group (CAG) which consisted of experts with knowledge of or practice experience in either: (a) services to youth with IDD, and/or (b) providing sex trafficking prevention or intervention services.

Participants and Procedures

Participants were identified using a combination of two sampling strategies—expert sampling and snowball sampling. Expert sampling solicits information from stakeholders with expertise or topical knowledge and can be used for SNA research when a complete list of organizations is difficult to obtain (Keszi et al., 2014). Snowball sampling is a form of convenience sampling that relies on initially selected participants to recommend other potential participants who fit the research criteria (Parker et al., 2019), and is a hallmark feature of SNA in which social networks are built based on existing relationships (Contandriopoulos et al., 2019).

The sampling frame consisted of 190 organizations in a southeastern state, and the first author consulted with the CAG to identify relevant organizations for the sampling frame. Organizations were limited to statewide organizations or those located in one of four counties in the central part of the state. This decision was based on feasibility constraints and the impact of geographic proximity on the likelihood of organizational collaboration (Jasny et al., 2019). Organizations were identified using the Google search engine by pairing terms for each of the four counties and key sectors (e.g., “urgent care,” “mental health services,” “disability services”). The first 10 pages were searched, and contact information was obtained directly from websites or resource directories provided by governmental agencies or task forces.

Participants were recruited from (and asked about their collaboration practices with) six sectors: (a) disability services (n = 47), (b) legal services and law enforcement (n = 14), (c) mental healthcare (n = 38), (d) medical healthcare (n = 42), (e) violence prevention and response (n = 25), and (f) other specialized populations or services (e.g., housing, food insecurity; n = 24). Selection of these six sectors was based on the needs and services most commonly identified in the sex trafficking prevention and response literature (Macy & Johns, 2011; Munro-Kramer et al., 2020) as well as the disability research and practice literature (Smith et al., 2020; Verdugo et al., 2020). Some organizations offered multiple types of services (e.g., a disability rights legal organization could be classified as disability services or legal services and law enforcement); the first author arranged and consolidated organizations into categories according to their overall mission and activities and received feedback on categorization from CAG members.

Of the 190 organizations in the initial sampling frame, 34 medical healthcare organizations did not have readily available email or telephone contact information. Therefore, a total of 156 organizations were recruited to participate in the study. Recruitment and survey administration occurred between June 2023 and September 2023. In an email invitation to participate, organizational representatives were provided information about the purpose of the study as well as a link to the survey. At the end of the survey, participants were offered a $15 Amazon gift card for their participation and given a link to a separate survey to provide their email address. A total of 47 organizations completed the survey (30% response rate).

Survey

Participants were invited to complete an online, 15 to 20 minutes survey programmed using Qualtrics survey software (Qualtrics Labs, Inc., 2020). The survey was developed and refined based on prior research examining organizational coordination in violence prevention and response, primarily building on the work of Cook-Craig (2010) and Menger et al. (2015). The study-developed, roster-based survey focused on stakeholder coordination and relationship strength within and across the six aforementioned organizational sectors. Participants were presented with a roster of all 190 organizations in the initial sampling frame and asked to select the organizations with which they coordinated during the past 24 months. To gather information about coordination, participants were then presented with an abbreviated list based on their selections, and were asked whether any of the following indicators of general collaboration and coordination had occurred within the past 24 months: (a) sent referrals, (b) received referrals, (c) shared information or resources, and (d) coordinated trainings. Possible responses included yes (coded as 1), no and unsure (coded as 0).

Participants were also asked about the relationship strength, specifically (a) organizations’ communication within the past 6 months, (b) their degree of confidence that they can rely on the selected organization to keep promises, and (c) their degree of trust that the selected organization can serve clients appropriately. Communication frequency was limited to six months to prevent recall issues associated with longer time references (Kobayashi & Boase, 2012), and responses were never (coded as 0), 1 to 5 times (coded as 1), 6 to 10 times (coded as 2), 11 to 15 times (coded as 3), 16 to 20 times (coded as 4), and more than 20 times (coded as 5). Possible responses for participants indicating their confidence in organizations keeping promises and trust in organizations to serve clients appropriately were strongly disagree (coded as 0), disagree (coded as 1), neutral (coded as 2), agree (coded as 3), and strongly agree (coded as 4). The survey item pertaining to confidence in organizations keeping promises was adapted from an inter-organizational trust measurement instrument (Zaheer et al., 1998). The survey item pertaining to trust in organizations to serve clients appropriately was developed for the current study based on its prominence in the social work literature, as well as the effect of trust in service provision on health outcomes (Thiede, 2005; Turner et al., 2023).

The final section featured questions pertaining to participant demographic and work history information. These questions included (a) age, (b) race/ethnicity, (c) gender identity, (d), educational background, (e) years of experience in providing services related to current role, (f) level of knowledge regarding minor sex trafficking dynamics and warning signs, and (g) level of knowledge regarding youth with intellectual and developmental disabilities and associated risks of experiencing sex trafficking.

Data Analysis

Data were exported from Qualtrics into Excel (Microsoft Corporation, 2017) for data management and then imported into the SNA software UCINET (Borgatti et al., 2002) for analysis of network properties, bivariate analyses, and network visualization. Stata 15.1 (StataCorp, 2017) was used for univariate analysis of demographic and work history data. In SNA, adjacency matrices are created to represent ties or relations between nodes or nodes (e.g., individuals or organizations), often using zero in a cell to indicate a tie is absent and one in a cell to indicate a tie is present. In the present study, nodes were organizations. Matrices can be directed (i.e., asymmetric) such that a tie is sent from one node to another node but not vice versa, or undirected (i.e., symmetric) with a tie being equally shared by both node (Hanneman & Riddle, 2005). All matrices representing the networks in this study were undirected except for two matrices—sending referrals and receiving referrals. To handle missing data from organizations that did not complete the survey, each undirected matrix was transposed (i.e., interchanged rows and columns); if Organization A (matrix row) indicated a tie existed with Organization B (matrix column), but Organization B did not complete the survey, then a tie was imputed between Organization B (matrix row) and Organization A (matrix column). The two directed matrices were transposed using their relational inverse (e.g., missing values were imputed into the receiving referrals matrix using the transpose of the sending referrals matrix). Additionally, to ensure consistency in responses, all undirected matrices were symmetrized such that either the average valued tie was used for interval data (i.e., communication frequency, organizational reliability to keep promises and trust to serve clients appropriately) or the maximum value of 1 was used for binary data (i.e., training coordination, sharing information and resources, and two-way referrals).

Network properties were examined using several techniques: (a) density (i.e., network connectivity measured via the proportion of present ties between organizations compared to all possible ties); (b) degree centrality (i.e., the extent to which organizations are connected to others); and (c) betweenness centrality (i.e., the amount of times an organization appears on the shortest path between otherwise disconnected organizations) (Borgatti et al., 2018). Visualizations of overall networks (i.e., all six sectors) and subnetworks (i.e., two to three sectors) were completed using the NetDraw feature available in UCINET. Subnetworks of violence prevention and response organizations and disability organizations were visualized given their relevancy to coordination of a response to sex trafficking among youth with IDD. To determine if organizations from other sectors (i.e., legal and law enforcement, medical healthcare, mental healthcare, and specialized populations or services) could serve as bridges (i.e., connection between segregated groups or clusters), these subnetworks were modified by selectively adding each of the four sectors to the visualizations.

Network property examination and the following analyses were conducted for the whole network of 190 organizations and a subnetwork consisting of only survey respondents (N = 47).

Bivariate Analyses

Three bivariate analyses (one monadic, two dyadic) were conducted. For each analysis, permutation tests were used given the violation of statistical assumptions regarding independence of observations and random sampling, as is typical with social network data.

Monadic (Organization-Level) Analysis With Analysis of Variance (ANOVA)

A series of one-way ANOVA models were conducted to test the mean difference of stakeholder coordination activities between organization types; the mean degree (i.e., number of connecting ties) of each stakeholder coordination activity was compared across the six sectors. To generate better estimates of standard error, 20,000 permutations were performed (Borgatti et al., 2018).

Dyadic (Relationship-Level) Analysis With Correlation

To test the association between relationship strength and stakeholder coordination networks, the Quadratic Assignment Procedure (QAP) correlation was used to compute Pearson's correlation coefficients between corresponding cells of two data matrices. The correlation was recomputed with 50,000 permutations to determine the proportion of times that a random correlation coefficient is larger than or equal to the correlation coefficient originally computed (Borgatti et al., 2002). To test the association between organizational similarity and either the stakeholder coordination or relationship strength networks, the organization type vector with values representing each of the six sectors was transformed into a binary matrix in which organization type matches were coded as 1 and non-matches were coded as 0. This meant that, if Organization A and Organization B were classified as the same organization type (e.g., disability services), the tie or cell was coded 1. If Organization A (e.g., disability services) was different from Organization C (e.g., legal services), the tie or cell was coded as 0.

Dyadic (Relationship-Level) Analysis With Regression

Based on statistically significant correlation results, the Double Dekker Semi-Partialling Multiple Regression QAP (MRQAP) was used to regress stakeholder coordination matrices on relationship strength matrices and the organizational similarity matrix. Similar to the QAP correlation procedure, a standard regression was performed across the corresponding cells of the dependent and independent matrices. The regression was then recomputed with 50,000 permutations to estimate standard errors for the statistics of interest; UCINET counts the proportion of random permutations that yielded a coefficient as extreme as the one originally computed (Borgatti et al., 2002).

Results

Descriptive Statistics

Descriptive information about individual survey participants are presented in Table 1. Most participants worked for disability-serving organizations (n = 16, 34%), followed by: mental healthcare (n = 9, 19.2%), specialized populations or services (n = 8, 17%), violence prevention and response (n = 7, 14.9%), legal services and law enforcement (n = 6, 12.8%), and medical healthcare (n = 1, 2.1%). Although the majority of organizations offered services for youth and adults with IDD (n = 36, 76.6%), only ten organizations (21.3%) offered services related to sex trafficking prevention. Nine organizations (19.1%) offered services for both. When asked about their level of knowledge regarding minor sex trafficking dynamics and warning signs, participants reported that they were very knowledgeable (n = 7, 15.2%), somewhat knowledgeable (n = 22, 47.8%), somewhat unknowledgeable (n = 9, 19.6%), and very unknowledgeable (n = 8, 17.4%). When asked about their level of knowledge regarding youth with IDD and associated risks of experiencing sex trafficking, participants reported that they were very knowledgeable (n = 6, 13.0%), somewhat knowledgeable (n = 23, 50.0%), somewhat unknowledgeable (n = 10, 21.7%), and very unknowledgeable (n = 7, 15.2%).

Table 1.
Descriptive Statistics for Individual Survey Participants.

Variable Frequency (N) Percent (%)

Age

25–34 years 5 11.63

35–49 years 24 55.81

50–64 years 11 25.58

65 years and older 3 6.98

Gender

Man 10 21.74

Non-binary 1 2.17

Woman 35 76.09

Race/Ethnicity

Asian 2 4.35

Black/African American 9 19.57

Latinx/Hispanic 3 6.52

Native American 1 2.17

White/Caucasian 32 69.57

Multi-racial 1 2.17

Professional Background

Education 9 19.57

Law 6 13.04

Medicine 3 6.52

Public Health 4 8.70

Psychology 9 19.57

Social Work 13 28.26

Sociology 1 2.17

Other 16 34.78

Time With Organization

1–10 years 20 43.48

11–20 years 11 23.91

21–30 years 13 28.26

31–40 years 1 2.17

41+ years 1 2.17

Note. N = 46 for all variables except age (N = 43).

Stakeholder Coordination

Network properties and monadic (organizational-level) analyses are discussed below according to each type of stakeholder coordination. Per recommendation of Borgatti et al. (2018), betweenness centrality was only calculated for symmetric networks. Density ties for each stakeholder coordination activity according to organization type are presented in Table 2 for the whole network, and in Table 3 for the survey respondent subnetwork.

Table 2.
Density Between and Within Organization Types—Whole Network.

D H L M S V

Training Coordination

D 0.291 0.083 0.280 0.029 0.452 0.125

H 0.000 0.000 0.150 0.400 0.571

L 0.321 0.317 0.235 0.395

M 0.297 0.483 0.417

S 0.333 0.516

V 0.517

Sharing Resources

D 0.713 0.667 0.538 0.529 0.795 0.719

H 0.000 1.000 0.750 0.700 0.857

L 0.933 0.878 0.824 0.953

M 0.784 1.000 0.917

S 0.905 1.000

V 0.897

Sending Referrals

D 0.496 0.333 0.654 0.358 0.406 0.281

H 0.583 0.500 0.111 0.950 0.500 0.857

L 0.240 0.222 0.280 0.268 0.529 0.651

M 0.521 0.800 0.415 0.757 0.586 0.694

S 0.469 0.700 0.353 0.828 0.524 0.677

V 0.613 0.571 0.605 0.806 0.419 0.603

Receiving Referrals

D 0.460 0.583 0.240 0.493 0.469 0.613

H 0.333 0.500 0.222 0.800 0.700 0.571

L 0.654 0.111 0.280 0.390 0.353 0.581

M 0.358 0.950 0.293 0.757 0.828 0.778

S 0.406 0.500 0.529 0.517 0.524 0.419

V 0.281 0.857 0.674 0.694 0.677 0.603

Two-Way Referrals

D 0.075 0.000 0.012 0.016 0.013 0.005

H 0.000 0.004 0.039 0.011 0.013

L 0.048 0.030 0.014 0.121

M 0.084 0.031 0.051

S 0.038 0.042

V 0.116

Note. D = Disability. H = Medical Healthcare. L = Legal and Law Enforcement. M = Mental Healthcare. S = Specialized Populations or Services. V = Violence Prevention and Response.

Table 3.
Density Between and Within Organization Types—Survey Respondent Network.

D H L M S V

Training Coordination

D 0.228 0.000 0.500 0.000 0.462 0.143

H 0.000 0.000 0.000 0.000 0.000

L 0. 375 0.333 0.286 0.471

M 0.167 0.889 0.300

S 0.600 0.250

V 0.500

Sharing Resources

D 0.709 0.000 0.625 0.647 0.846 0.875

H 0.000 0.000 0.000 1.000 0.000

L 1.000 0.833 0.714 1.000

M 0.667 1.000 0.909

S 1.000 1.000

V 0.857

Sending Referrals

D 0.513 0.000 0.625 0.353 0.400 0.125

H 0.000 0.000 0.000 1.000 0.000 0.000

L 0.286 0.000 0.438 0.333 0.571 0.706

M 0.588 1.000 0.333 0.667 0.444 0.545

S 0.600 0.000 0.571 0.778 0.500 0.833

V 0.625 0.000 0.647 0.909 0.500 0.571

Receiving Referrals

D 0.426 0.000 0.286 0.471 0.600 0.625

H 0.000 0.000 0.000 1.000 0.000 0.000

L 0.625 0.000 0.438 0.250 0.571 0.588

M 0.353 1.000 0.417 0.667 0.778 0.818

S 0.400 0.000 0.571 0.222 0.500 0.500

V 0.125 0.000 0.765 0.545 0.833 0.571

Two-Way Referrals

D 0.158 0.000 0.021 0.021 0.023 0.000

H 0.000 0.000 0.111 0.000 0.000

L 0.133 0.056 0.042 0.262

M 0.042 0.042 0.095

S 0.036 0.054

V 0.190

Note. D = Disability; H = Medical Healthcare; L = Legal and Law Enforcement; M = Mental Healthcare; S = Specialized Populations or Services; V = Violence Prevention and Response.

Referrals

Sending Referrals

More than 77% (n = 147) of organizations sent referrals within the past 24 months (see Figure 1), and the density for the network was 0.53. The one-way ANOVA results showed that average degree significantly differed across organization type, F(5, 184) = 2.62, p < .05. On average, organizations sent referrals to approximately three organizations as indicated by the mean degree centrality (M = 3.71, SD = 0.50). A violence prevention and response organization and mental health organization sent the most referrals, with 38 ties and 31 ties, respectively. The third highest number of ties came from a disability organization (28 ties). Twenty-eight ties connected 53 disability and violence prevention and response organizations in a subnetwork of these two sectors; more referrals were sent from violence prevention and response organizations to disability organizations (19 ties) than from disability organizations to violence prevention and response organizations (9 ties) (see Figure 2). According to Table 2, 61.3% of possible ties existed from violence prevention and response organizations to disability organizations and 28.1% of possible ties existed from disability organizations to violence prevention and response organizations. For the survey respondent subnetwork (shown in Figure 3), one-way ANOVA results suggest that average degree did not significantly differ across organization type, F(5, 41) = 2.36, p = .07. The density of the subnetwork was 0.53. The mean degree centrality for this subnetwork was 4.30 (SD = 0.50).

Figure 1.
Sending Referrals—Whole Network. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 2.
Sending Referrals—Violence Prevention and Response and Disability Organizations. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 3.
Sending Referrals—Survey Respondent Subnetwork. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement).

Receiving Referrals

Most (77%, n = 147) organizations received referrals within the past 24 months, and the density for the network was 0.52 (see Figure 4). The one-way ANOVA model comparing organization types’ average degree was statistically significant, F(5,184) = 3.95, p < .01. The mean degree centrality (M = 3.63, SD = 0.50) indicated that the average organization received referrals from approximately three organizations. The same mental health organization that sent the second highest number of referrals received the most referrals (30 ties), while a disability organization had the second highest ties (27 ties). When examining the network restricted to 53 disability organizations and violence prevention and response organizations, 28 ties connect the two sectors (see Figure 5). Disability organizations received 19 referrals from violence prevention and response organizations while violence prevention and response organizations received nine referrals from disability organizations. Density ties between these two sectors were the inverse for sending referrals. For the survey respondent subnetwork (shown in Figure 6), the one-way ANOVA results were not statistically significant for organization type, F(5, 41) = 1.69, p = .15. The density of the subnetwork was 0.49. The mean degree centrality for this subnetwork was 3.98 (SD = 0.50).

Figure 4.
Receiving Referrals—Whole Network. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 5.
Receiving Referrals—Violence Prevention and Response and Disability Organizations. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 6.
Receiving Referrals—Survey Respondent Subnetwork. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement).

Two-way Referrals

Nearly 65% (n = 123) of organizations engaged in two-way (i.e., sending and receiving referrals) coordination (see Figure 7). The average betweenness centrality for the network was 85.34 (SD = 249.24). Only 3.1% of possible ties existed in this network. The one-way ANOVA results indicate that there was a statistically significant difference in average degree among the organization types, F(5,184) = 2.91, p < .05. Calculation of the mean degree centrality (M = 2.51, SD = 0.17) showed that organizations had, on average, two bidirectional referral ties. The highest number of ties was for a mental health organization (30 ties), followed by a violence prevention and response organization (25 ties). The subnetwork of 43 disability organizations and violence prevention and response organizations shows only three ties connecting these sectors, with only 0.5% of possible ties existing (see Figure 8). For the survey respondent subnetwork (shown in Figure 9), the one-way ANOVA results were not statistically significant, F(5, 41) = 1.25, p = .30. The density of the subnetwork was 0.06. The betweenness centrality for the average organization was 67.87 (SD = 119.10). The mean degree centrality for this subnetwork was 2.85 (SD = 0.24).

Figure 7.
Two-Way Referrals—Whole Network. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black Up Triangle = Legal or Law Enforcement). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 8.
Two-Way Referrals—Violence Prevention and Response and Disability Organizations. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 9.
Two-Way Referrals—Survey Respondent Subnetwork. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement).

Sharing Information and Resources

Over 75% (n = 143) of organizations engaged in sharing information and resources as shown in Figure 10. The betweenness centrality for the average organization in the overall network was 88.44 (SD = 215.64). Of all possible ties, 78.8% were present. Results from the one-way ANOVA indicated that there was a statistically significant difference in average degree among organization type, F(5, 184) = 7.22, p < .001. According to the mean degree centrality (M = 5.64, SD = 0.41), organizations had about five ties, on average. The same violence prevention and response organization and mental health organization that had the highest number of two-way referrals also participated in the most information and resource sharing, with 39 ties for the violence prevention and response organization and 34 ties for the mental health organization. Fifty-four disability and violence prevention and response organizations engaged in sharing information and resources with 23 ties connecting these two sectors (see Figure 11). Most disability and violence prevention and response organizations exchanged information and resources with 71.9% of possible ties present. The presence of specialized, mental healthcare, and legal organizations appear to establish more indirect ties between disability and violence prevention and response organizations. For the survey respondent subnetwork (shown in Figure 12), the one-way ANOVA results were not statistically significant, F(5, 41) = 1.63, p = .17. The density of the subnetwork was 0.81. The betweenness centrality for the average organization was 27.21 (SD = 34.74). The mean degree centrality for this subnetwork was 6.60 (SD = 0.39).

Figure 10.
Sharing Information/Resources—Whole Network. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 11.
Sharing Information/Resources—Violence Prevention and Response and Disability Organizations. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 12.
Sharing Information/Resources—Survey Respondent Subnetwork. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement).

Training Coordination

Over 59% (n = 113) of organizations engaged in training coordination in the whole network as depicted in Figure 13. The average betweenness centrality for the networks was 79.11 (SD = 234.90). The density of the whole network is 0.30, suggesting that a little under one-third of all possible ties existed. A one-way ANOVA revealed that at least two organization types significantly differed in the average degree or number of training coordination ties, F(5, 184) = 4.49, p < .01. When calculating degree centrality, on average organizations had approximately two ties (M = 2.17, SD = 0.46) with the highest degree centrality for a mental health organization (25 ties) followed by the same violence prevention and response organization that had the most information and resource sharing ties (24 ties). The highest number of ties for a disability organization was 15 ties. Forty disability and violence prevention and response organizations engaged in training coordination with four ties connecting these two sectors (see Figure 14). As noted in Table 2, 12.5% of possible training coordination ties existed between disability organizations and violence prevention and response organizations. The presence of specialized and legal organizations established more indirect ties between disability and violence prevention and response organizations. For the survey respondent subnetwork (shown in Figure 15), the one-way ANOVA results were not statistically significant, F(5, 41) = 1.37, p = .26. The density of the subnetwork was 0.32. The betweenness centrality for the average organization was 51.81 (SD = 110.25). The mean degree centrality for this subnetwork was 2.64 (SD = 0.46).

Figure 13.
Training Coordination—Whole Network. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 14.
Training Coordination—Violence Prevention and Response and Disability Organizations. Note. Node Color and Shape: Sector (Red circle = Disability; Green diamond = Violence Prevention and Response). Isolates (Nodes Without Ties) Deleted for Readability.

Figure 15.
Training Coordination—Survey Respondent Subnetwork. Note. Node Color and Shape: Sector (Red Circle = Disability; Green Diamond = Violence Prevention and Response; Blue Plus = Medical Healthcare; Purple Square = Specialized Populations or Services; Gray Down Triangle = Mental Healthcare; Black up Triangle = Legal or Law Enforcement).

Relationship Strength

Findings for each relationship strength indicator for the overall network and the six organizational sectors are presented below.

Communication Frequency

The majority of organizations (78.4%, n = 149) in the network communicated at least once within the past 6 months; 55 organizations in a subnetwork of disability organizations and violence prevention and response organizations communicated at least once in this timeframe. On average, organizations communicated approximately either one to five times or six to ten times (M = 1.76, SD = 1.36). Over one-third of the network (38.4%, n = 73) engaged in communication at least 11 times in the past 6 months; there was only one communication tie at this frequency between the disability organizations and violence prevention and response organizations. One specialized population or services organization and one legal organization served as ties connecting the disability and violence prevention and response sectors.

Confidence in Organizational Promises

On average, organizations tended to have confidence that others in the network could reliably keep promises (M = 2.94, SD = 0.93). Only 16 organizations had negative ties (i.e., never, strongly disagreed, or disagreed that others were reliable), with one violence prevention and response organization having four ties characterized as negative. When restricting the network to the 112 organizations that had very positive ties (i.e., strongly agreed that others were reliable), disability organizations and medical healthcare organizations were the least likely to have very positive ties with other sectors; only two very positive ties existed between disability organizations and violence prevention and response organizations.

Organizational Trust to Serve Clients

Organizations generally agreed that they trusted others in the network to serve clients appropriately (M = 3.05, SD = 0.92). Very few (n = 15) organizations had negative ties (i.e., never, strongly disagreed, or disagreed that others were trustworthy), with the same aforementioned violence prevention and response organization having three negative ties. Visual inspection of the network consisting of 119 organizations with very positive ties (i.e., strongly agreed that others were trustworthy) shows that disability organizations had very positive ties primarily with mental health organizations. Three very positive ties connected the disability and violence prevention and response sectors, including the same two strong ties pertaining to organizational reliability.

Dyadic Analyses

For the first dyadic (relationship-level) analysis, Pearson correlations between the matrices for the stakeholder coordination, relationship strength indicators, and organizational similarity are presented in Table 4 (whole network) and Table 5 (survey respondent subnetwork). In the whole network, the only relationship strength indicator that was significantly correlated with all of the stakeholder coordination activities was communication frequency, in particular training coordination (r = 0.51, p < .05) and two-way referrals (r = 0.46, p < .001). Unsurprisingly, the three referral networks were significantly associated with one another; otherwise, there were no significant associations between stakeholder coordination activities. The two-way referral network was significantly correlated with all stakeholder coordination and relationship strength networks, as well as organizational similarity. Although organizational similarity was not significantly associated with stakeholder coordination beyond two-way referrals, it was significantly associated with all three relationship strength indicators. Reliability on organizational promises and organizational trust to serve clients appropriately were the only significant associations among relationship strength networks. Findings were similar for the survey respondent subnetwork, with communication frequency serving as the only statistically significant relationship strength indicator of stakeholder coordination activities.

Table 4.
Correlation Coefficients for Coordination and Relational Indicators—Whole Network.

CF OS RP RR SI SR TC TR TS

CF 1.000 0.112* 0.301 0.312* 0.320* 0.313* 0.506* 0.457 0.269

OS 1.000 0.133 −0.015 −0.015 −0.001 0.031 0.113 0.183

RP 1.000 0.093 0.175 0.087 0.264 0.212 0.827

RR 1.000 0.146 0.344* 0.208 0.686 0.078

SI 1.000 0.154 0.321 0.207 0.117

SR 1.000 0.207 0.685 0.082

TC 1.000 0.314 0.182

TR 1.000 0.177**

TS 1.000

Note. CF = Communication Frequency; OS = Organizational Similarity; RP = Organizational Reliability on Promises; RR = Receive Referrals; SI = Sharing Information and Resources; SR = Send Referrals; TC = Training Coordination; TR = Two-Way Referrals; TS = Organizational Trust to Serve Clients.

*
p < .05.

**
p < .001.

Table 5.
Correlation Coefficients for Coordination and Relational Indicators—Survey Respondent Network.

CF OS RP RR SI SR TC TR TS

CF 1.000 −0.074 0.311* 0.297* 0.302* 0.306* 0.409* 0.518* 0.262

OS 1.000 0.123 0.014 −0.036 −0.024 −0.054 −0.006 0.068

RP 1.000 0.099 0.137 0.085 0.178 0.202 0.803***

RR 1.000 0.133 0.302* 0.198 0.629*** 0.071

SI 1.000 0.159 0.311* 0.164* 0.117

SR 1.000 0.190 0.627* 0.087

TC 1.000 0.338* 0.089

TR 1.000 0.156

TS 1.000

Note. CF = Communication Frequency; OS = Organizational Similarity; RP = Organizational Reliability on Promises; RR = Receive Referrals; SI = Sharing Information and Resources; SR = Send Referrals; TC = Training Coordination; TR = Two-Way Referrals; TS = Organizational Trust to Serve Clients.

*
p < .05.

p < .01.

*
p < .001.

For the second dyadic (relationship-level) analysis, each stakeholder coordination activity was regressed on communication frequency and organizational similarity (see Tables 6 and 7). Two-way referrals were regressed on all three relationship strength indicators and organizational similarity. Communication frequency was a statistically significant predictor for all forms of stakeholder coordination in both the whole network and subnetwork of survey respondents. When removing the non-significant predictors in each regression model, the adjusted R² values suggest that communication frequency can explain the following amount of variation in each outcome: (a) 25.4% in training coordination, (b) 10.1% in sharing information and resources, (c) 9.7% in sending referrals, (d) 9.6% in receiving referrals, and (e) 20.8% in two-way referrals. Further, when limiting analyses to survey respondents, the adjusted R² values suggest that communication frequency can explain the following amount of variation in each outcome: (a) 16.3% in training coordination, (b) 8.7% in sharing information and resources, (c) 9.1% in sending referrals, (d) 8.6% in receiving referrals, and (e) 26.4% in two-way referrals.

Table 6.
Regression of Coordination on Relational Indicators—Whole Network.

Outcome Predictor b SE β

Training Coordination^a Constant 0.011* 0.000

Communication Frequency 0.171* 0.013 0.509

Organizational Similarity −0.028 0.033 −0.029

Sharing Information^b Constant 0.635* 0.000

Communication Frequency 0.097* 0.011 0.327

Organizational Similarity −0.045 0.031 −0.053

Sending Referrals^c Constant 0.338* 0.000

Communication Frequency 0.116* 0.012 0.317

Organizational Similarity −0.036 0.031 −0.035

Receiving Referrals^d Constant 0.331* 0.000

Communication Frequency 0.116* 0.012 0.317

Organizational Similarity −0.050 0.031 −0.048

Two-Way Referrals^e Constant −0.029* 0.000

Communication Frequency 0.149* 0.014 0.429

Organizational Similarity 0.004 0.036 0.004

Reliability on Promises 0.045 0.033 0.087

Trust to Serve Clients −0.004 0.033 −0.008

Note.

^a
Adjusted-R² = 0.254.

^b
Adjusted-R² = 0.103.

^c
Adjusted-R² = 0.098.

^d
Adjusted-R² = 0.098.

^e
Adjusted-R² = 0.206.

*
p < .001.

Table 7.
Regression of Coordination on Relational Indicators—Survey Respondent Network.

Outcome Predictor b SE β

Training Coordination^a Constant 0.067* 0.000

Communication Frequency 0.151* 0.028 0.408

Organizational Similarity −0.028 0.080 −0.013

Sharing Information^b Constant 0.648* 0.000

Communication Frequency 0.095* 0.024 0.301

Organizational Similarity −0.013 0.071 −0.013

Sending Referrals^c Constant 0.322* 0.000

Communication Frequency 0.122* 0.022 0.306

Organizational Similarity 0.003 0.062 0.002

Receiving Referrals^d Constant 0.276* 0.000

Communication Frequency 0.120* 0.022 0.301

Organizational Similarity 0.051 0.063 0.040

Two-Way Referrals^e Constant −0.027* 0.000

Communication Frequency 0.195* 0.030 0.510

Organizational Similarity 0.092 0.082 0.074

Reliability on Promises 0.021 0.056 0.041

Trust to Serve Clients −0.006 0.056 −0.011

Note.

^a
Adjusted-R² = 0.159.

^b
Adjusted-R² = 0.082.

^c
Adjusted-R² = 0.089.

^d
Adjusted-R² = 0.085.

^e
Adjusted-R² = 0.254.

*
p < .05.

p < .01.

*
p < .001.

Discussion and Applications to Practice

The literature has largely focused on individuals with disabilities as a unit of analysis and their perspectives regarding service inaccessibility following sexual assault or domestic violence (Chirwa et al., 2020; Robinson et al., 2021; Stern et al., 2020). This innovative study focused on service organizations as a unit of analysis and employed SNA to explore their engagement in specific forms of coordination. Prior research suggests that identifying and enhancing inter-organizational collaboration may improve service accessibility and thus subsequent health outcomes of individuals with IDD (Roberge et al., 2016). Key findings and implications of this research are discussed below.

For each stakeholder coordination activity, several of the same organizations (one mental health, one disability, and one violence prevention and response) had the highest degree centrality, meaning that they possessed the most ties to other organizations. These three particular organizations may be best positioned to garner buy-in from others when forming a coalition or task force (Brown et al., 2010). Violence prevention and response organizations sent more referrals to disability organizations than the inverse. While the nature of these referrals is unknown, it is possible that (a) clients required disability-related services as a result of violence, or (b) practitioners affiliated with violence prevention and response organizations lack expertise in providing services to clients with disabilities. Previous studies have found that sexual assault survivors with disabilities are more likely than survivors without disabilities to be referred to victim services by social service organizations and other formal sources (e.g., law enforcement); survivors without disabilities are more likely to disclose to and be referred by friends (Campbell et al., 2021; Grossman & Lundy, 2008). It is important for disability, legal, and specialized organizations to recognize their role in securing services for violence victims with IDD as they are less likely to self-refer or seek assistance from informal peer networks. To improve the referring relationship in a coordinated response to sex trafficking, organizations must know how to identify victimization and which organizations in the community provide services accordingly.

The highest amount of stakeholder coordination was for sharing information and resources, which complements the findings of Menger et al.'s (2015) research on organizational collaboration in a suicide prevention network. While the high density of ties between disability and violence prevention and response organizations indicates a well-connected subnetwork, all others sectors (though less so for medical healthcare) appeared as conduits of information and resource flow in network visualizations. In other words, mental healthcare, legal and law enforcement, and specialized organizations can connect disability and violence prevention and response organizations via information and resource sharing. Agencies should consider how information exchange can improve case management while not compromising client confidentiality and data security.

Of all stakeholder coordination activities, training coordination was the least reported by the overall network as well as the subnetwork of disability organizations and violence prevention and response organizations. Recent studies have highlighted training gaps in law enforcement agencies (Railey et al., 2020) and medical settings (Sapp et al., 2021) regarding the service needs of individuals with disabilities. Though less has been published about disability training needs among violence prevention and response organizations, visual representations of the training coordination network in the current study suggest that specialized and legal organizations can serve as potential brokers. Brokers are facilitators that can link disconnected organizations and are vital to information flow between groups (Long et al., 2013; Reus et al., 2023). It should be noted that, while cross-sector training was not as widely reported as other types of coordination, this does not mean that organizations are not receiving training internally or from other sources. Cross-sector training in anti-sex trafficking initiatives benefits community responses to sex trafficking, particularly in the actualization of shared goals and improved communication (Jones & Lutze, 2016; Nichols et al., 2023).

Two interesting findings were the statistically significant and non-significant associations for organization type and organizational similarity, respectively. The average degree or number of connections with others was significantly associated with organization type (i.e., organizations belonging to any one of the six sectors) in the whole network, but sharing information and resources, referrals, and training coordination were not significantly associated with organizational similarity (i.e., organizations’ match in organization type). That is, it does not appear that organizations belonging to the same service category engage in more stakeholder coordination than organizations that provide different services. For instance, violence prevention and response organizations may engage in more information and resource sharing than legal organizations, but that does not mean that more information and resource sharing occurs between violence prevention and response organizations and other violence prevention and response organizations. This finding is encouraging as it demonstrates the possibility of interdisciplinary collaboration.

Another prominent finding was the significant effect of communication frequency on all forms of coordination. Results from the regression models suggest that how often organizations communicate with one another is related to their engagement in coordination. This aligns with Menger et al.'s (2015) findings in that communication frequency was significantly correlated with information and resource sharing, making referrals, and training coordination. In the current study, violence prevention and response organizations and disability organizations rarely engaged in high frequency communication with each other. In fact, the network visualization showed high frequency within the disability sector, whereas violence prevention and response organizations were more integrated with other sectors. Considering the contribution of communication frequency to general coordination, it would befit organizations to determine if lack of communication with other sectors is expected due to the nature of their work and opportunities for solidifying relationships such as community outreach events.

The two relationship indicators that were strongly significantly correlated with each other were organizations’ trust in others to serve clients appropriately and confidence in others’ keeping promises. Both survey items provide a nuanced understanding of trust and appear to align with definitions posed by Gulati and Sytch (2008). Namely, trust to serve clients may be described as dispositional trust (i.e., expectations about other organizations’ general trustworthiness) while reliability of promises may be described as relational trust (i.e., trust that another organization can reliably fulfill its obligations; Gulati & Sytch, 2008). However, in contrast to Menger et al.'s (2015) findings, trust was not significantly associated with any of the coordination activities. A longer history of interaction between organizations can lead to a higher level of trust (Gulati & Sytch, 2008). Information not collected in this study was length of time that organizations had been engaged in various coordination activities. Future research requires a more robust disentanglement of trust with consideration for its antecedence.

One notable practice-related implication of this study is the importance of dedicated job positions to inter-organizational collaboration in the form of boundary spanners. Boundary spanners are individuals whose professional role involves engaging in multi-agency and multi-sectoral activities and processes; in social and healthcare services, these are typically case managers (Gittell & Weiss, 2004; Huang et al., 2016; Williams, 2011). Organizations should engage in ongoing evaluation to determine existing gaps in inter-organizational coordination in conjunction with client needs, thereby delegating specific tasks to the boundary spanner. For example, a social worker in a medical setting may request information and resources from a disability-serving organization upon an influx of clients with comorbid IDD and chronic illnesses (Findley, 2014). Moreover, identification of boundary spanners has implications for the selection of key personnel for task forces and coalitions. This is relevant to a coordinated response to sex trafficking considering the interdisciplinary nature of these partnerships (Miller et al., 2016). Boundary spanners are invaluable to organizational collaboration and suggest that interpersonal networks can also be informative. Future research can explore the influence of interpersonal ties on inter-organizational coordination (Huang et al., 2016). Lastly, organizations should clarify in their mission statements and action plans the extent to which they practice collaboration with other agencies and its role in enhancing client well-being. Part of this clarification may involve an organizational culture and value assessment, as cultural differences between organizations and disagreement over shared values or goals can obstruct effective coordination (Gulati et al., 2012).

This explorative study is not without several limitations. First, the network was geographically limited to a metropolitan area in a southeastern state. Thus, network characteristics cannot be generalized and will vary according to resource availability within regional areas (e.g., rural, urban) and outside the state. An education sector was not included in the sampling frame and survey. Within the scope of sex trafficking prevention, schools play a critical role in providing prevention programming and identifying students who are at-risk (Rizo et al., 2019; Walker et al., 2022). However, four agencies that provide special education services to youth were included in the study as disability-serving organizations. In light of recent research that examines the role of special education staff in sex trafficking prevention efforts (Jackson, 2022), public schools and other educational institutions should be included in research that investigates their coordination patterns with other professional sectors. The survey had low response rates, and another limitation was the low participation among healthcare organizations due to recruitment challenges. Despite this, the use of matrix transposition to impute missing values is a strength of SNA and allowed for the opportunity to determine how healthcare organizations engage in stakeholder coordination with other sectors. The classification of organization types was a subjective process, particularly for organizations providing services across various sectors. Input from a diverse group of stakeholders in the form of a community advisory group sought to address this limitation. Furthermore, when asked about the relationship strength of their organizations, very few participants said that other organizations were distrustful or unreliable. Only two questions pertaining to trust were included to reduce survey burden. However, organizational trust is a multi-faceted construct (Zaheer et al., 1998), and further research is needed to understand how different forms of trust are positively or negatively associated with coordination.

A well-coordinated approach to minor sex trafficking prevention and response involves multiple community partners, trust building, and ongoing communication and collaboration (Nichols et al., 2023). Following these recommendations, this study sought to examine organizational collaboration across six service sectors, specifically their engagement in various coordination activities and the effect of communication, trust, and reliability. Communication frequency was strongly associated with making referrals, sharing information and resources, and coordinating trainings. Future investigations can utilize other SNA techniques such as exponential random graph models (ERGMs) to track changes in coordination activity engagement and organizational relationship strength over time. Findings from this study can influence development of a cohesive community response to sex trafficking that is responsive to the service needs of youth with IDD.

Variable	Frequency (N)	Percent (%)
Age
25–34 years	5	11.63
35–49 years	24	55.81
50–64 years	11	25.58
65 years and older	3	6.98
Gender
Man	10	21.74
Non-binary	1	2.17
Woman	35	76.09
Race/Ethnicity
Asian	2	4.35
Black/African American	9	19.57
Latinx/Hispanic	3	6.52
Native American	1	2.17
White/Caucasian	32	69.57
Multi-racial	1	2.17
Professional Background
Education	9	19.57
Law	6	13.04
Medicine	3	6.52
Public Health	4	8.70
Psychology	9	19.57
Social Work	13	28.26
Sociology	1	2.17
Other	16	34.78
Time With Organization
1–10 years	20	43.48
11–20 years	11	23.91
21–30 years	13	28.26
31–40 years	1	2.17
41+ years	1	2.17

	D	H	L	M	S	V
Training Coordination
D	0.291	0.083	0.280	0.029	0.452	0.125
H		0.000	0.000	0.150	0.400	0.571
L			0.321	0.317	0.235	0.395
M				0.297	0.483	0.417
S					0.333	0.516
V						0.517
Sharing Resources
D	0.713	0.667	0.538	0.529	0.795	0.719
H		0.000	1.000	0.750	0.700	0.857
L			0.933	0.878	0.824	0.953
M				0.784	1.000	0.917
S					0.905	1.000
V						0.897
Sending Referrals
D	0.496	0.333	0.654	0.358	0.406	0.281
H	0.583	0.500	0.111	0.950	0.500	0.857
L	0.240	0.222	0.280	0.268	0.529	0.651
M	0.521	0.800	0.415	0.757	0.586	0.694
S	0.469	0.700	0.353	0.828	0.524	0.677
V	0.613	0.571	0.605	0.806	0.419	0.603
Receiving Referrals
D	0.460	0.583	0.240	0.493	0.469	0.613
H	0.333	0.500	0.222	0.800	0.700	0.571
L	0.654	0.111	0.280	0.390	0.353	0.581
M	0.358	0.950	0.293	0.757	0.828	0.778
S	0.406	0.500	0.529	0.517	0.524	0.419
V	0.281	0.857	0.674	0.694	0.677	0.603
Two-Way Referrals
D	0.075	0.000	0.012	0.016	0.013	0.005
H		0.000	0.004	0.039	0.011	0.013
L			0.048	0.030	0.014	0.121
M				0.084	0.031	0.051
S					0.038	0.042
V						0.116

	D	H	L	M	S	V
Training Coordination
D	0.228	0.000	0.500	0.000	0.462	0.143
H		0.000	0.000	0.000	0.000	0.000
L			0. 375	0.333	0.286	0.471
M				0.167	0.889	0.300
S					0.600	0.250
V						0.500
Sharing Resources
D	0.709	0.000	0.625	0.647	0.846	0.875
H		0.000	0.000	0.000	1.000	0.000
L			1.000	0.833	0.714	1.000
M				0.667	1.000	0.909
S					1.000	1.000
V						0.857
Sending Referrals
D	0.513	0.000	0.625	0.353	0.400	0.125
H	0.000	0.000	0.000	1.000	0.000	0.000
L	0.286	0.000	0.438	0.333	0.571	0.706
M	0.588	1.000	0.333	0.667	0.444	0.545
S	0.600	0.000	0.571	0.778	0.500	0.833
V	0.625	0.000	0.647	0.909	0.500	0.571
Receiving Referrals
D	0.426	0.000	0.286	0.471	0.600	0.625
H	0.000	0.000	0.000	1.000	0.000	0.000
L	0.625	0.000	0.438	0.250	0.571	0.588
M	0.353	1.000	0.417	0.667	0.778	0.818
S	0.400	0.000	0.571	0.222	0.500	0.500
V	0.125	0.000	0.765	0.545	0.833	0.571
Two-Way Referrals
D	0.158	0.000	0.021	0.021	0.023	0.000
H		0.000	0.000	0.111	0.000	0.000
L			0.133	0.056	0.042	0.262
M				0.042	0.042	0.095
S					0.036	0.054
V						0.190

	CF	OS	RP	RR	SI	SR	TC	TR	TS
CF	1.000	0.112*	0.301	0.312*	0.320*	0.313*	0.506*	0.457**	0.269
OS		1.000	0.133**	−0.015	−0.015	−0.001	0.031	0.113**	0.183**
RP			1.000	0.093	0.175	0.087	0.264	0.212**	0.827**
RR				1.000	0.146	0.344*	0.208	0.686**	0.078
SI					1.000	0.154	0.321	0.207**	0.117
SR						1.000	0.207	0.685**	0.082
TC							1.000	0.314**	0.182
TR								1.000	0.177**
TS									1.000

	CF	OS	RP	RR	SI	SR	TC	TR	TS
CF	1.000	−0.074	0.311*	0.297*	0.302*	0.306*	0.409*	0.518***	0.262
OS		1.000	0.123	0.014	−0.036	−0.024	−0.054	−0.006	0.068
RP			1.000	0.099	0.137	0.085	0.178	0.202**	0.803***
RR				1.000	0.133	0.302*	0.198	0.629***	0.071
SI					1.000	0.159	0.311*	0.164***	0.117
SR						1.000	0.190	0.627***	0.087
TC							1.000	0.338***	0.089
TR								1.000	0.156**
TS									1.000

Outcome	Predictor	b	SE	β
Training Coordination^a	Constant	0.011*	0.000
	Communication Frequency	0.171*	0.013	0.509
	Organizational Similarity	−0.028	0.033	−0.029
Sharing Information^b	Constant	0.635*	0.000
	Communication Frequency	0.097*	0.011	0.327
	Organizational Similarity	−0.045	0.031	−0.053
Sending Referrals^c	Constant	0.338*	0.000
	Communication Frequency	0.116*	0.012	0.317
	Organizational Similarity	−0.036	0.031	−0.035
Receiving Referrals^d	Constant	0.331*	0.000
	Communication Frequency	0.116*	0.012	0.317
	Organizational Similarity	−0.050	0.031	−0.048
Two-Way Referrals^e	Constant	−0.029*	0.000
	Communication Frequency	0.149*	0.014	0.429
	Organizational Similarity	0.004	0.036	0.004
	Reliability on Promises	0.045	0.033	0.087
	Trust to Serve Clients	−0.004	0.033	−0.008

Outcome	Predictor	b	SE	β
Training Coordination^a	Constant	0.067***	0.000
	Communication Frequency	0.151***	0.028	0.408
	Organizational Similarity	−0.028	0.080	−0.013
Sharing Information^b	Constant	0.648***	0.000
	Communication Frequency	0.095***	0.024	0.301
	Organizational Similarity	−0.013	0.071	−0.013
Sending Referrals^c	Constant	0.322***	0.000
	Communication Frequency	0.122***	0.022	0.306
	Organizational Similarity	0.003	0.062	0.002
Receiving Referrals^d	Constant	0.276***	0.000
	Communication Frequency	0.120***	0.022	0.301
	Organizational Similarity	0.051	0.063	0.040
Two-Way Referrals^e	Constant	−0.027***	0.000
	Communication Frequency	0.195***	0.030	0.510
	Organizational Similarity	0.092	0.082	0.074
	Reliability on Promises	0.021	0.056	0.041
	Trust to Serve Clients	−0.006	0.056	−0.011

Footnotes

Acknowledgments

The authors would like to thank the community advisory group and study participants for their time and contribution to this research.

Author Note

Melissa R. Jenkins are now affiliated with Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.

ORCID iD

Melissa R. Jenkins

Ethical Considerations

Study methods were reviewed and approved by the Office of Human Research Ethics at the University of North Carolina at Chapel Hill (#23-0481).

Consent to Participate

Participants were provided information about the research process at the beginning of the survey. Upon reviewing the information, all participants provided written informed consent prior to participating.

Consent for Publication

Participants provided consent to publish research findings.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this study was supported by the Melissa Institute for Violence Prevention and Treatment [Belfer Aptman Scholarship]. Support was also provided to the first author through the Eunice Kennedy Shriver National Institute of Child Health and Human Development [grant number T32 HD007489].

Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The participants of this study did not give written consent for their data to be shared beyond what is reported in the publication.

References

Bono

L. K.

Haverkamp

C. R.

Lindsey

R. A.

Freedman

R. N.

McClain

M. B.

Simonsmeier

(2022). Assessing interdisciplinary trainees’ objective and self-reported knowledge of autism spectrum disorder and confidence in providing services. Journal of Autism and Developmental Disorders, 52(1), 376–391. https://doi.org/10.1007/s10803-021-04948-3

Borgatti

S. P.

Everett

M. G.

Freeman

L. C.

(2002). UCINET 6 for windows (Version 6.730). Analytic Technologies. https://sites.google.com/site/ucinetsoftware/home.

Borgatti

S. P.

Everett

M. G.

Johnson

J. C.

(2018). Analyzing social networks (2nd ed.). Sage.

Bovat

Haskin

Hinman

Latomaa

(2022). 2020 National Data and Assessment Survey [Report]. National Human Trafficking and Disability Working Group. https://iofa.org/2022/11/national-data-and-assessment-survey/ .

Brown

L. D.

Feinberg

M. E.

Greenberg

M. T.

(2010). Determinants of community coalition ability to support evidence-based programs. Prevention Science, 11(3), 287–297. https://doi.org/10.1007/s11121-010-0173-6.

Bunger

A. C.

(2013). Administrative coordination in nonprofit human service delivery networks: The role of competition and trust. Nonprofit and Voluntary Sector Quarterly, 42(6), 1155–1175. https://doi.org/10.1177/0899764012451369

Campbell

Javorka

Gregory

Vollinger

(2021). Supporting sexual assault survivors with disabilities: Tracing disclosure and referral pathways to postassault health care services. American Journal of Orthopsychiatry, 91(6), 751–762. https://doi.org/10.1037/ort0000576

Carrellas

Resko

S. M.

Day

A. G.

(2021). Sexual victimization and intellectual disabilities among child welfare involved youth. Child Abuse & Neglect, 115, Article 104986. https://doi.org/10.1016/j.chiabu.2021.104986

Chen

Y. H.

Lin

T. P.

Yen

D. C.

(2014). How to facilitate inter-organizational knowledge sharing: The impact of trust. Information & Management, 51(5), 568–578. https://doi.org/10.1016/j.im.2014.03.007

10.

Chesworth

B. R.

Rizo

C. F.

Franchino-Olsen

Klein

L. B.

Macy

R. J.

Martin

S. L.

(2020). Protocol schools can use to report commercial sexual exploitation of children to child protective services. School Social Work Journal, 45(1), 40–57. https://www.ingentaconnect.com/contentone/iassw/sswj/2020/00000045/00000001/art00006

11.

Chirwa

Jewkes

Van Der Heijden

Dunkle

(2020). Intimate partner violence among women with and without disabilities: A pooled analysis of baseline data from seven violence-prevention programmes. BMJ Global Health, 5(11), e002156. https://doi.org/10.1136/bmjgh-2019-002156

12.

Colvin

M. L.

Thompson

H. M.

Cooley

M. E.

(2021). The ‘cost’ of collaborating and other challenges in inter-organizational child welfare practice: A community-wide perspective. Journal of Public Child Welfare, 15(5), 617–651. https://doi.org/10.1080/15548732.2020.1778597

13.

Contandriopoulos

Sapeha

Larouche

(2019). Some insights related to social network analysis data collection challenges—A research note. International Journal of Social Research Methodology, 22(5), 463–468. https://doi.org/10.1080/13645579.2019.1574957

14.

Cook-Craig

P. G.

(2010). Using social network theory to influence the development of state and local primary prevention capacity-building teams. Journal of Family Social Work, 13(4), 313–325. https://doi.org/10.1080/10522158.2010.492497

15.

Findley

P. A.

(2014). Social work practice in the chronic care model: Chronic illness and disability care. Journal of Social Work, 14(1), 83–95. https://doi.org/10.1177/1468017313475381

16.

Franchino-Olsen

Silverstein

H. A.

Kahn

N. F.

Martin

S. L.

(2020). Minor sex trafficking of girls with disabilities. International Journal of Human Rights in Healthcare, 13(2), 97–108. https://doi.org/10.1108/IJHRH-07-2019-0055

17.

Franchino-Olsen

Silverstein

H. A.

Kahn

N. F.

Martin

S. L.

(2022). Physical disabilities and low cognitive ability increase odds of minor sex exchange among adolescent males in the United States. Journal of Child Sexual Abuse, 31(8), 967–986. https://doi.org/10.1080/10538712.2022.2146560

18.

Fraser-Barbour

E. F.

(2018). On the ground insights from disability professionals supporting people with intellectual disability who have experienced sexual violence. The Journal of Adult Protection, 20(5/6), 207–220. https://doi.org/10.1108/JAP-04-2018-0006

19.

Gerassi

Nichols

Michelson

(2017). Lessons learned: Benefits and challenges in interagency coalitions addressing sex trafficking and commercial sexual exploitation. Journal of Human Trafficking, 3(4), 285–302. https://doi.org/10.1080/23322705.2016.1260345

20.

Gerassi

L. B.

Cheng

S. Y.

Muentner

Benson

(2021). Prevalence and associated characteristics of youth who trade sex in a representative sample of high school students. Journal of Adolescence, 93(1), 1–9. https://doi.org/10.1016/j.adolescence.2021.09.008

21.

Gest

S. D.

Osgood

D. W.

Feinberg

M. E.

Bierman

K. L.

Moody

(2011). Strengthening prevention program theories and evaluations: Contributions from social network analysis. Prevention Science, 12(4), 349–360. https://doi.org/10.1007/s11121-011-0229-2

22.

Gittell

J. H.

Weiss

(2004). Coordination networks within and across organizations: A multi-level framework. Journal of Management Studies, 41(1), 127–153. https://doi.org/10.1111/j.1467-6486.2004.00424.x

23.

Grizzle

Goodin

Robinson

S. E.

(2020). Connecting with new partners in COVID-19 response. Public Administration Review, 80(4), 629–633. https://doi.org/10.1111/puar.13247

24.

Grossman

S. F.

Lundy

(2008). Double jeopardy: A comparison of persons with and without disabilities who were victims of sexual abuse and/or sexual assault. Journal of Social Work in Disability & Rehabilitation, 7(1), 19–46. https://doi.org/10.1080/15367100802009715

25.

Gulati

Sytch

(2008). Does familiarity breed trust? Revisiting the antecedents of trust. Managerial and Decision Economics, 29(2-3), 165–190. https://doi.org/10.1002/mde.1396

26.

Gulati

Wohlgezogen

Zhelyazkov

(2012). The two facets of collaboration: Cooperation and coordination in strategic alliances. Academy of Management Annals, 6(1), 531–583. https://doi.org/10.5465/19416520.2012.691646

27.

Hanneman

R. A.

Riddle

(2005). Introduction to social network methods. University of California, Riverside. http://faculty.ucr.edu/∼hanneman/ .

28.

Harmon-Darrow

Rubenstein

Burruss-Cousins

McTavish

Eisler

Finigan-Carr

(2023). Engaging victims of child sex trafficking: Training for child welfare workers. Social Work Research, 47(3), 171–181. https://doi.org/10.1093/swr/svad008

29.

Harper

E. A.

(2013). School-based prevention of commercial sexual exploitation: A special focus on the needs of and effective services for at-risk urban African American girls (Publication No. 3571374) [Doctoral dissertation, Georgia State University]. ProQuest Dissertations Publishing. https://www.proquest.com/dissertations-theses/school-based-prevention-commercial-sexual/docview/1426824496/se-2

30.

Huang

Luo

Liu

Yang

(2016). An investigation of interpersonal ties in interorganizational exchanges in emerging markets: A boundary-spanning perspective. Journal of Management, 42(6), 1557–1587. https://doi.org/10.1177/0149206313511115

31.

Jackson

E. E.

(2022). Student entry to domestic minor sex trafficking and the role of special education staff (Publication No. 29323241) [Doctoral dissertation, University of Kansas]. ProQuest Dissertations Publishing. https://www.proquest.com/dissertations-theses/student-entry-domestic-minor-sex-trafficking-role/docview/2719023059/se-2

32.

Jasny

Johnson

Campbell

L. K.

Svendsen

Redmond

(2019). Working together: The roles of geographic proximity, homophilic organizational characteristics, and neighborhood context in civic stewardship collaboration networks in Philadelphia and New York city. Ecology and Society, 24(4), Article 8. https://doi.org/10.5751/ES-11140-240408

33.

Jones

(2023). Perceptions of the benefits and barriers to anti-human trafficking interagency collaboration: An exploratory factor analysis study. Societies, 13(2), Article 38. https://doi.org/10.3390/soc13020038

34.

Jones

T. R.

Lutze

F. E.

(2016). Anti-human trafficking interagency collaboration in the state of Michigan: An exploratory study. Journal of Human Trafficking, 2(2), 156–174. https://doi.org/ 10.1080/23322705.2015.1075342

35.

Kembro

Selviaridis

Naslund

(2014). Theoretical perspectives on information sharing in supply chains: A systematic literature review and conceptual framework. Supply Chain Management: An International Journal, 19(5-6), 609–625. https://doi.org/10.1108/SCM-12-2013-0460

36.

Keszi

Judit

Papp

(2014). Cooperation and exclusion: An explorative analysis of collaborative networks of Hungarian disability organizations. Corvinus Journal of Sociology and Social Policy, 5(1), 143–161. https://doi.org/10.14267/2014.01.06

37.

Kobayashi

Boase

(2012). No such effect? The implications of measurement error in self-report measures of mobile communication use. Communication Methods and Measures, 6(2), 126–143. https://doi.org/10.1080/19312458.2012.679243

38.

Kruger

Harper

Harris

Sanders

Levin

Meyers

(2013). Sexualized and dangerous relationships: Listening to the voices of low-income African American girls placed at risk for sexual exploitation. Western Journal of Emergency Medicine, 14(4), 370–376. https://doi.org/10.5811%2Fwestjem.2013.2.16195

39.

Lavoie

Dickerson

K. L.

Redlich

A. D.

Quas

J. A.

(2019). Overcoming disclosure reluctance in youth victims of sex trafficking: New directions for research, policy, and practice. Psychology, Public Policy, and Law, 25(4), 225–238. https://doi.org/10.1037/law0000205

40.

P. D.

Ryan

Rosenstock

Goldmann

(2018). Health issues associated with commercial sexual exploitation and sex trafficking of children in the United States: A systematic review. Behavioral Medicine, 44(3), 219–233. https://doi.org/10.1080/08964289.2018.1432554

41.

Levine

J. A.

(2017). Mental health issues in survivors of sex trafficking. Cogent Medicine, 4(1), Article 1278841. https://doi.org/10.1080/2331205X.2017.1278841

42.

Bland

Bibal

Chavoshan

Chung

Davis

P. M.

Lewis

G. R.

Mendelson

Millard

Oldach

Porter

L. D. R.

Paik-Tesch

Greenbaum

V. J.

Chambers

(2022). Training residents on understanding trafficked humans (TRUTH). Journal of Human Trafficking, 8(3), 282–293. https://doi.org/10.1080/23322705.2020.1794746

43.

Long

J. C.

Cunningham

F. C.

Braithwaite

(2013). Bridges, brokers and boundary spanners in collaborative networks: A systematic review. BMC Health Services Research, 13, Article 158. https://doi.org/10.1186/1472-6963-13-158

44.

Macy

R. J.

Johns

(2011). Aftercare services for international sex trafficking survivors: Informing US service and program development in an emerging practice area. Trauma, Violence, & Abuse, 12(2), 87–98. https://doi.org/10.1177/1524838010390709

45.

Marburger

Pickover

(2020). A comprehensive perspective on treating victims of human trafficking. Professional Counselor, 10(1), 13–24. https://doi.org/10.15241/km.10.1.13

46.

Margolis

L. H.

Rosenberg

Umble

Chewning

(2013). Effects of interdisciplinary training on MCH professionals, organizations and systems. Maternal and Child Health Journal, 17(5), 949–958. https://doi.org/10.1007/s10995-012-1078-8

47.

Menger

L. M.

Stallones

Cross

J. E.

Henry

K. L.

Chen

P. Y.

(2015). Strengthening suicide prevention networks: Interorganizational collaboration and tie strength. Psychosocial Intervention, 24(3), 155–165. https://doi.org/10.1016/j.psi.2015.07.005

48.

Mevissen

de Jongh

(2010). PTSD And its treatment in people with intellectual disabilities: A review of the literature. Clinical Psychology Review, 30(3), 308–316. https://doi.org/10.1016/j.cpr.2009.12.005

49.

Microsoft Corporation . (2017). Microsoft excel version 4011577 [computer software]. Microsoft Corporation.

50.

Miller

C. L.

Duke

Northam

(2016). Child sex-trafficking recognition, intervention, and referral: An educational framework for the development of health-care-provider education programs. Journal of Human Trafficking, 2(3), 177–200. https://doi.org/10.1080/23322705.2015.1133990

51.

Muftić

L. R.

Finn

M. A.

(2013). Health outcomes among women trafficked for sex in the United States: A closer look. Journal of Interpersonal Violence, 28(9), 1859–1885. https://doi.org/10.1177/0886260512469102

52.

Munro-Kramer

Beck

Choi

Singer

Gebhard

Carr

(2020). Human trafficking victim’s service needs and outcomes: An analysis of clinical law data. Journal of Human Trafficking, 6(1), 95–108. https://doi.org/10.1080/23322705.2019.1574476

53.

Nazer

(2017). Medical evaluation and management of sexual abuse in children with disabilities. Journal of Alternative Medicine Research, 9(3), 249–256. https://novapublishers.com/shop/volume-9-issue-3-2017-journal-of-alternative-medicine-research/

54.

Nezami

M. R.

de Bruijne

M. L.

Hertogh

M. J.

Bakker

H. L.

(2023). Inter-Organizational collaboration in interconnected infrastructure projects. Sustainability, 15(8), Article e6721. https://doi.org/10.3390/su15086721

55.

Nichols

Slutsker

Oberstaedt

Gilbert

(2023). Team approaches to addressing sex trafficking of minors: Promising practices for a collaborative model. Societies, 13(3), Article e66. https://doi.org/10.3390/soc13030066

56.

Nixon-Cream

(2019). A qualitative study of interagency collaboration among organizations serving human trafficking victims (Publication No. 27544711) [Doctoral dissertation, Capella University]. ProQuest Dissertations Publishing. https://www.proquest.com/dissertations-theses/qualitative-study-interagency-collaboration-among/docview/2312908444/se-2

57.

Ouakouak

M. L.

Ouedraogo

(2019). Fostering knowledge sharing and knowledge utilization: The impact of organizational commitment and trust. Business Process Management Journal, 25(4), 757–779. https://doi.org/10.1108/BPMJ-05-2017-0107

58.

Parker

Scott

Geddes

(2019). Snowball sampling. In Atkinson

Delamont

Cernat

Sakshaug

J. W.

Williams

R. A.

(Eds.), SAGE Research Methods Foundations. Sage. https://doi.org/10.4135/9781526421036831710

59.

Polaris Project . (2020). Myths, facts, and statistics. https://polarisproject.org/myths-facts-and-statistics/ .

60.

Prell

Hubacek

Reed

(2009). Stakeholder analysis and social network analysis in natural resource management. Society & Natural Resources, 22(6), 501–518. https://doi.org/10.1080/08941920802199202

61.

Puranam

Raveendran

(2013). Interdependence and organization design. In Grandori

(Ed.), Handbook of economic organization: Integrating economic and organization theory (pp. 193–209). Edward Elgar Publishing. https://doi.org/10.4337/9781782548225 .

62.

Qualtrics Labs, Inc . (2020). Version September 2023 of the qualtrics research suite. Qualtrics Labs, Inc. http://www.qualtrics.com .

63.

Railey

K. S.

Bowers-Campbell

Love

A. M.

Campbell

J. M.

(2020). An exploration of law enforcement officers’ training needs and interactions with individuals with autism spectrum disorder. Journal of Autism and Developmental Disorders, 50(1), 101–117. https://doi.org/10.1007/s10803-019-04227-2

64.

Reid

J. A.

Strauss

Haskell

R. A.

(2018). Clinical practice with commercially sexually exploited girls with intellectual disabilities. In Nichols

A. J.

Edmond

Heil

E. C.

(Eds.), Social work practice with survivors of sex trafficking and commercial sexual exploitation (pp. 218–238). Columbia University Press. https://doi.org/10.7312/nich18092-012

65.

Reus

Moser

Groenewegen

(2023). Knowledge sharing quality on an enterprise social network: Social capital and the moderating effect of being a broker. Journal of Knowledge Management, 27(11), 187–204. https://doi.org/10.1108/JKM-02-2023-0115

66.

Richie-Zavaleta

A. C.

Villanueva

Martinez-Donate

Turchi

R. M.

Ataiants

Rhodes

S. M.

(2020). Sex trafficking victims at their junction with the healthcare setting—A mixed-methods inquiry. Journal of Human Trafficking, 6(1), 1–29. https://doi.org/10.1080%2F23322705.2018.1501257

67.

Rizo

C. F.

Klein

L. B.

Chesworth

B. R.

O’Brien

J. E.

Macy

R. J.

Martin

S. L.

Crews

M. E.

Love

B. L.

(2019). Educating youth about commercial sexual exploitation of children: A systematic review. Global Social Welfare, 6(1), 29–39. https://doi.org/10.1007/s40609-018-0119-7

68.

Roberge

Hudon

Pavilanis

Beaulieu

M. C.

Benoit

Brouillet

Boulianne

De Pauw

Frigon

Gaboury

Gaudreault

Girard

Giroux

Grégoire

Langlois

Lemieux

Loignon

Vanasse

(2016). A qualitative study of perceived needs and factors associated with the quality of care for common mental disorders in patients with chronic diseases: The perspective of primary care clinicians and patients. BMC Family Practice, 17(1), 1–14. https://doi.org/10.1186/s12875-016-0531-y

69.

Robinson

Frawley

Dyson

(2021). Access and accessibility in domestic and family violence services for women with disabilities: Widening the lens. Violence Against Women, 27(6–7), 918–936. https://doi.org/10.1177/1077801220909890

70.

Rowan

N. L.

Gillette

P. D.

Faul

A. C.

Yankeelov

P. A.

Borders

K. W.

Deck

Nicholas

L. D.

Wiegand

(2009). Innovative interdisciplinary training in and delivery of evidence-based geriatric services: Creating a bridge with social work and physical therapy. Gerontology & Geriatrics Education, 30(3), 187–204. https://doi.org/10.1080/02701960903133448

71.

Roy

Lemieux

A. J.

Gagnon

J. P.

Crocker

A. G.

(2023). Lessons learned from the implementation of an intersectoral cross-training approach to improve justice outcomes for homeless adults with mental illness. Journal of Interprofessional Education & Practice, 32, Article e100641. https://doi.org/10.1016/j.xjep.2023.100641

72.

Sapp

R. W.

Sebok-Syer

S. S.

Gisondi

M. A.

Rotoli

J. M.

Backster

McClure Poffenberger

(2021). The prevalence of disability health training and residents with disabilities in emergency medicine residency programs. AEM Education and Training, 5(2), Article e10511. https://doi.org/10.1002/aet2.10511

73.

Scott

(2020). Identifying human trafficking victims in the pediatric and school nurse practice setting. Pediatric Nursing, 46(6), 278–281. https://www.proquest.com/docview/2471026870

74.

Shah

G. H.

Badana

A. N.

Robb

Livingood

W. C.

(2016). Cross-jurisdictional resource sharing in changing public health landscape: Contributory factors and theoretical explanations. Journal of Public Health Management and Practice, 22(2), 110–119. https://doi.org/ 10.1097/PHH.0000000000000368

75.

Smith

K. A.

Gehricke

J. G.

Iadarola

Wolfe

Kuhlthau

K. A.

(2020). Disparities in service use among children with autism: A systematic review. Pediatrics, 145(Suppl. 1), S35–S46. https://doi.org/10.1542/peds.2019-1895g

76.

Soylu

Alpaslan

A. H.

Ayaz

Esenyel

Oruç

(2013). Psychiatric disorders and characteristics of abuse in sexually abused children and adolescents with and without intellectual disabilities. Research in Developmental Disabilities, 34(12), 4334–4342. https://doi.org/10.1016/j.ridd.2013.09.010

77.

StataCorp . (2017). Stata statistical software (Version 15) [Computer software]. StataCorp LLC.

78.

Stern

van der Heijden

Dunkle

(2020). How people with disabilities experience programs to prevent intimate partner violence across four countries. Evaluation and Program Planning, 79, Article e101770. https://doi.org/10.1016/j.evalprogplan.2019.101770

79.

Strike

D. L.

Skovholt

T. M.

Hummel

T. J.

(2004). Mental health professionals’ disability competence: Measuring self-awareness, perceived knowledge, and perceived skills. Rehabilitation Psychology, 49(4), 321–327. https://doi.org/10.1037/0090-5550.49.4.321

80.

Thiede

(2005). Information and access to health care: Is there a role for trust? Social Science & Medicine, 61(7), 1452–1462. https://doi.org/10.1016/j.socscimed.2004.11.076

81.

Todres

(2010). Taking prevention seriously: Developing a comprehensive response to child trafficking and sexual exploitation. Vanderbilt. Journal of Transnational Law, 43(1), 1–56. https://search.informit.org/doi/ https://doi.org/10.3316/agispt.20101565

82.

Trafficking Victims Protection Reauthorization Act of 2017, 22 USC § 7102 . (2017). https://www.govinfo.gov/content/pkg/BILLS-115s1862enr/pdf/BILLS-115s1862enr.pdf

83.

Turner

N. R.

Freitag

Johnson

Parsey

C. M.

Ramirez

Berridge

(2023). The role of trust in older adult service provision at the onset of the COVID-19 pandemic. Journal of Gerontological Social Work, 66(6), 739–762. https://doi.org/10.1080/01634372.2022.2164821

84.

Twis

Cimino

A. N.

Plunk

Mendenhall

Mancus

(2025). Correlates of youth sex trafficking in the United States: The role of the environment in shaping vulnerability and demand. Trauma, Violence, & Abuse, Advance online publication. https://doi.org/10.1177/15248380251317214

85.

Valaitis

Cleghorn

Ploeg

Risdon

Mangin

Dolovich

Agarwal

Oliver

Gaber

Chung

(2020). Disconnected relationships between primary care and community-based health and social services and system navigation for older adults: A qualitative descriptive study. BMC Family Practice, 21(1), Article e69. https://doi.org/10.1186/s12875-020-01143-8

86.

Verdugo

M. A.

Aguayo

Arias

V. B.

García-Domínguez

(2020). A systematic review of the assessment of support needs in people with intellectual and developmental disabilities. International Journal of Environmental Research and Public Health, 17(24), Article e9494. https://doi.org/10.3390/ijerph17249494

87.

Victims of Trafficking and Violence Protection Act of 2000, 22 USC § 7102 (2000). https://www.govinfo.gov/app/details/USCODE-2000-title22/USCODE-2000-title22-chap78-sec7102/summary .

88.

Walker

Michener

Ieva

(2022). Do you see us?: The need for school-based sex trafficking prevention programs for Black girls. Journal of African American Women and Girls in Education, 2(1), 82–100. https://doi.org/10.21423/jaawge-v2i1a36

89.

Williams

(2011). The life and times of the boundary spanner. Journal of Integrated Care, 19(3), 26–33. https://doi.org/ 10.1108/14769011111148140

90.

Yang

T. M.

Maxwell

T. A.

(2011). Information-sharing in public organizations: A literature review of interpersonal, intra-organizational and inter-organizational success factors. Government Information Quarterly, 28(2), 164–175. https://https://doi.org/ /10.1016/j.giq.2010.06.008

91.

Zaheer

McEvily

Perrone

(1998). Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organization Science, 9(2), 141–159. https://doi.org/10.1287/orsc.9.2.14