Community coalitions are regarded as a viable mechanism for the scale-up and long-term dissemination of prevention services (Butterfoss, 2006; Rugs, Hills, Moore, & Peters, 2011). Coalitions are multi-disciplinary, they bring diverse stakeholders together, and may help to coordinate and streamline resources (Goodman, 2000; Hays, Hays, DeVille, & Mulhall, 2000; Wolff, 2001). Although some research suggests that community coalitions are ineffective at achieving important outcomes (Hallfors, Cho, Livert, & Kadushin, 2002; Roussos & Fawcett, 2000; Saxe et al., 2006), at least two coalition based models, Communities that Care (CTC) and PROSPER (PROmoting School-community-university Partnerships to Enhance Resilience) indicate that coalition based delivery of EBIs can yield public health impacts for youth and families and be sustained over time (Oesterle et al., 2018; Spoth et al., 2015). Both PROSPER and CTC include technical assistance for program implementation, sustainability, and the overall functioning of coalitions.

Community coalitions implementing EBIs are faced with a myriad of tasks, many of which follow a developmental sequence (Butterfoss, Goodman, & Wandersman, 1993; Florin, Mitchell, Stevenson, & Klein, 2000; Hawkins, Catalano, & Arthur, 2002; Livet & Wandersman, 2005). We apply a three-stage model to our work in PROSPER (Chilenski et al., 2016; Feinberg, Chilenski, Greenberg, Spoth, & Redmond, 2007). The early stage of coalition development, organization, involves forming the coalition and tasks that organize the coalition into a working cohesive body. For example, roles and operating procedures are established. Goals and priorities are clarified. The second stage, called operations, includes tasks associated with EBI implementation and fidelity monitoring, such as hiring, training, and supervising facilitators, recruiting participants, scheduling observations, and more. We consider the third stage sustainment, or successful continuation of the initiative at high levels of quality. Although sustaining the effort needs to be considered from the beginning, a coalition’s operations change once one to two “implementation years” have been conducted and a routine has been established. Implementation of programs can become routine in this stage. New activities such as raising money and in-kind resources, increasing awareness of the effort and programs, and communicating activities and results to key stakeholders becomes important (Chinman, Imm, & Wandersman, 2004).

The ways in which coalitions function across these stages appears related to important outcomes, including both EBI implementation quality and sustainability. For example, in a study of CTC sites, coalition functioning and sustainability planning predicted coalition sustainability over a 4-year period (Feinberg, Bontempo, & Greenberg, 2008), and in PROSPER, readiness predicted to the quality of coalition functioning during the organizational stage (Greenberg, Feinberg, Meyer-Chilenski, Spoth, & Redmond, 2007), but not into the operations stage (Feinberg et al., 2007). Other work indicates that higher levels of funding, stronger leadership, more efficiency in coalition activities, strong relationships both within the coalition and with external partners, and fidelity to the CTC model were associated with better EBI implementation (Brown, Feinberg, Shapiro, & Greenberg, 2013).

Although it is widely believed that providing technical assistance (TA) improves implementation quality and contributes to sustainability of innovations (Fixsen et al., 2005; Mihalic & Irwin, 2003), data describing TA, and their impacts are relatively scarce (Katz & Wandersman, 2016). Appropriately, much of the early work came from qualitative or mixed methods observational research on community coalitions (Florin, Mitchell, & Stevenson, 1993; Goodman, Steckler, Hoover, & Schwartz, 1993; Kegler, Steckler, Malek, & McLeroy, 1998). Little is known about how variations in the frequency and quality of TA affect the activities of coalitions. Some research suggests that effective TA is organized around specific tasks of coalition work, effective cultivation of relationships both internal and external to the coalition, and the developmental stages of the coalition, and that these factors also relate to TA dosage and mode of delivery (Katz & Wandersman, 2016; Wandersman et al., 2008).

A few empirical studies have examined characteristics of TA and its relation to coalition functioning and program implementation. The links between dosage (typically defined as the number of TA contacts provided to an implementer or coalition) and outcomes such as program implementation fidelity, sustainability, and coalition functioning are inconsistent. Chinman et al. (2008) analyzed relations between TA dosage provided to sites in the Getting to Outcomes evaluation and found that dosage was positively related to quality of performing some tasks (such as developing an outcome evaluation), but not to others (such as use of best practices), while other research found no relations (Keener, 2007; Mitchell, Stone-Wiggins, Stevenson, & Florin, 2004). An additional study of CTC coalitions by Feinberg et al. (2008) found a moderated relation between dosage and the length of time a coalition had been operating, with newer coalitions more impacted by TA than those that had been in operation longer.

Other studies generally find that face-to-face contact is more strongly linked to coalition outcomes than phone or e-mail contacts (Becker, Lynde, & Swanson, 2008; Feinberg, Ridenour, et al., 2008; Ray, Wilson, Wandersman, Meyers, & Katz, 2012), while a relationship variable such as collaboration between the TA provider and the coalition leader (i.e., “working alliance” (DiGirolamo & Tkach, 2019) positively related to later coalition functioning (Chilenski et al., 2016). Some research indicates that proactive, anticipatory TA initiated by the provider was important given that implementers rated lower in capacity were less likely to request support (Fagan, Hanson, Hawkins, & Arthur, 2008; Kegeles, Rebchook, & Tebbetts, 2005). Another qualitative study also found that the provider--program implementer relationship was central to effective TA (Satterlund, Treiber, Kipke, Kwon, & Cassady, 2013).

Because coalitions typically progress through developmental stages and confront different challenges and tasks at each stage, it seems reasonable to assume that the amount of TA will vary by stage. Indeed, a number of researchers advocate tailoring TA to the developmental stage (Florin et al., 1993; Wandersman, Chien, & Katz, 2012). While conceptual frameworks such as Getting to Outcomes describe variations in TA related to developmental stages of coalitions (Wandersman et al., 2012), with the exception of our earlier work (Chilenski et al., 2018), we found very little empirical data involving changes over time in dosage or quality of TA, or ways in which this variability might be related to outcomes such as coalition functioning or program implementation quality.

Given the substantial resources devoted to TA and the growing demand for EBIs in applied settings, more information on specific TA characteristics, as well as the relations among these variables and outcomes, is needed. Because the literature on TA delivery over time and subsequent coalition outcomes is limited, the goal of the current study was to examine longitudinal relations between TA dosage and the subsequent functioning of community coalitions.

In the current study, we investigate the ways in which TA dosage, as measured by the average frequency of contact between the TA provider and the coalition/coalition leader in a given month, related to the functioning of community coalitions. We expected that greater frequency of TA would be positively associated with better coalition functioning, including positive interpersonal dynamics, stronger leadership, better organization procedures, greater focus on task completion, and reduced conflict. We expected that associations between TA dosage and coalition functioning would be invariant across all stages. However, because the literature on TA indicates that it varies by developmental stage, we hypothesized that the frequency of TA would vary by stage. As in prior work, we identified three stages in PROSPER: organizational, operations, and sustainment (Chilenski et al., 2018). We hypothesized that coalitions would receive more TA at the beginning of each stage, when tasks were new and unfamiliar.

Because the amount of contact between TA providers and coalitions is so fundamental, we first investigated the associations of the frequency of TA with both concurrent and future community coalition functioning. After assessing the longitudinal associations between contact and coalition functioning, we explored changes over time in the frequency of contact. We explored our hypotheses utilizing 4.5 years of data spanning the organizational, operations, and sustainment stages of coalitions.

PROSPER coalitions were comprised of community stakeholders interested in youth-focused prevention and were led by a Cooperative Extension Educator and a representative of the local public school district. PROSPER was embedded within the University-based Extension system for four main reasons. First, Extension is the only organization with a direct connection to the research expertise of the land grant university and has a history of effective and extensive collaborative networking among community agencies and institutions who serve youth and families (Coward, VanHorn, & Jackson, 1986). Extension also has a long history of providing educational programming for youth and families in communities (Mincemoyer, Perkins, & Lillehoj, 2004). Second, Extension has a long-standing implementation support infrastructure for county Extension educators through state-level faculty on campus such as regular learning communities and in-service trainings. Third, Extension Educators typically hold MS degrees and have expertise and experience in applied, community-based programming. Additionally, at the time of PROSPER’s initiation, the use of evidence-based programming was prioritized by Extension (Spoth et al., 2004).

The Extension leader and school co-leader were responsible for building a cohesive and representative coalition, including identifying and recruiting other coalition members. Typically, community coalitions included 10–12 individuals from organizations including the local school district, mental health and substance abuse service agencies, youth-serving organizations such as the YMCA or the United Way, faith-based organizations, local government, the business community, and law enforcement. Some coalitions also included youth and parent members. Leading the community coalition was part of the Extension Educator’s paid job. Some coalition members in schools and community agencies were able to integrate their PROSPER responsibilities, such as attending meetings and contributing to PROSPER goals, into their regular job; others were volunteers. Once formed, all 14 PROSPER coalitions attended a two-day training focused on coalition building, developing a shared vision, understanding the research and theory behind implementation of evidence-based programs, and communication with the broader community.

In PROSPER intervention communities, each community coalition chose one family-focused and one school-based program from a small list of universal, substance misuse prevention interventions with strong evidence of effectiveness. The family-based options, which included Strengthening Families Program for Parents and Youth ages 10–14 (SFP:10–14; (Iowa State University Extension, 1995), the Adolescent Transitions Project (Dishion, Andrews, Kavanagh, & Soberman, 1996), and Preparing for the Drug Free Years (later renamed Guiding Good Choices; (Park et al., 2000), were extracurricular programs targeted toward 6^th grade youth and their parents. The school-based programs, Life Skills Training (Botvin, Baker, Dusenbury, Tortu, & Botvin, 1990), Project Alert (Ellickson, 1984), and All Stars (Hansen & Dusenbury, 2004) were targeted toward 7^th graders and were taught by classroom teachers as part of the school curriculum. Thus, the school-based program reached virtually every 7^th grade youth in the participating school district, while the family-focused program reached a much smaller proportion (approximately 17% project-wide) of the target population. All community coalitions selected SFP:10–14 as their family-focused program in both Pennsylvania and Iowa, but there was some variability in which school-based program was adopted.

The programs were widely embraced by the target communities. Their emphasis on resilience and positive youth development, being able to choose the program, and including feedback from school personnel, parents, and at times youth, were reasons why this occurred. Implementing the programs within a coalition context theoretically increases the support base of stakeholders across the community. School personnel were particularly appreciative that the PROSPER programs met both their goals for outreach to parents and the standards for health education outlined by the local school board and the Pennsylvania Department of Education. Communities were also attracted to the resources that PROSPER offered, including training, program materials, and ongoing technical assistance.

The PROSPER technical assistance model, in which the Prevention Coordinator provides direct technical assistance to community coalitions, was comprehensive. The Prevention Coordinator is the linking agent between the community coalition and the resources and knowledge found within the university setting or other state-level resources (Spoth et al., 2004). Each coalition was assigned a consistent Prevention Coordinator (25% FTE). The Prevention Coordinators were expected to check in with each community coalition a minimum of three times each month, typically with two phone calls and attend the monthly in-person coalition meeting. During the phone calls, the Prevention Coordinators would review the community coalition’s recent activities, providing “on the spot” technical assistance as appropriate in the form of problem solving or brainstorming solutions. The Prevention Coordinator and Community Coalition Leader would also talk about and plan next steps. At community coalition meetings, the Prevention Coordinator would often answer questions about collaboration or evidence-based programming and prevention science, among other topics providing other expert guidance as needed. Other additional, as-needed communications with coalition leaders or members in the form of email or phone check-ins also occurred and were expected. For example, at times this included activities such as connecting the coalition leader to key stakeholders or being a partner when conducting outreach to key stakeholders in the community. Prevention Coordinators would often also approach individuals at the University-level for advice or direction, develop materials or informational resources, or provide feedback on recruitment materials or other promotional materials. In between phone or in-person meetings, the Prevention Coordinator would also need to conduct follow-up activities related to their next steps and prepare for future contacts.

Other technical assistance activities included an annual meeting of Prevention Coordinators, state-level and university-level faculty or staff, coalition members, and coalition leaders. Professional learning community meetings began after 2.5 years. Both the annual meeting and the learning communities promoted professional development, collaborative learning, and strategic planning. Prevention Coordinators received weekly supervision and support from University/state-level prevention researchers and Extension Faculty. See Chilenski et al. (2016) for more details.

Data for this project were collected over a 4.75 year period from Prevention Coordinators and community coalition members. Table 1 presents an overview of the project timeline and assessment schedule. The organizational stage covered the first 9 months of the project, and included three measurement occasions (i.e., waves) from Prevention Coordinators, and two from community prevention coalition members. The operations stage covered the next 1.5 years, including six waves of data collection by Prevention Coordinators and one from community prevention coalition members. The sustainment stage started after the operations stage, 2.25 years into the project. Data for this study were collected for another 2.50 years which included three waves of data from community prevention coalition members and five waves of data from their Prevention Coordinators.

All scales were standardized by taking the mean of the scale items and then aggregated to the coalition-level. See Table 2 for descriptive statistics.

See Table 2. Descriptive statistics show that the communities in this sample have low levels of poverty (M = 6.67%, SD = 2.52). Across the study, Prevention Coordinators most often reported having contact with their coalitions once per week. The second most frequently endorsed response was twice per month. Ratings of internal coalition functioning were quite positive and seem to decrease slightly over time.

Table 3 summarizes Spearman Correlations with the frequency of contact. During the organizational phase, there were few sizeable associations. Contrary to our predictions, a high frequency of contact predicted lower ratings of team focus on work and higher levels of tension at early stages of coalition development. However, these associations changed at later waves. The frequency of contact during the organizational stage predicted positive ratings of internal coalition processes during the operations stage: ratings of leadership, culture, goals, and team focus on work were all correlated with dosage at or above the .35 threshold. Correlations were not as consistent at Wave 4 (early sustainment), with only team focus on work reaching threshold. Correlations were consistently positive and strong at Wave 5 (mid-sustainment) and also at Wave 6 (later-sustainment).

Correlations with frequency of contact during the implementation phase also predicted positive ratings of team leadership, team culture, and team work focus within that phase. Associations were weaker at Wave 4, but became stronger at Wave 5, predicting more positive ratings of culture, goals, and team work focus, and lower ratings of tension. Four meaningful associations remained at Wave 6; the frequency of contact predicted higher ratings of team culture and goals, and lower ratings of tension.

Correlations of contact during early sustainment also predicted higher ratings of team leadership, culture, goals, and team work focus, and lower ratings of tension at Wave 5, mid-sustainment. Three meaningful associations remained at Wave 6. The frequency of contact related to higher ratings of team leadership and goals, and lower ratings of team tension.

We next examined the best longitudinal model of contact over time. Table 4 displays the results of this hypothesis which include the model fit statistics for the baseline, random intercept-only (i.e. empty) model, and the two next best-fitting models. The best fitting model to describe the frequency of contact over time is the random intercept-only model. In other words, the best longitudinal model is one that estimates only a stable and consistent frequency of contact for each community over time. The predicted probabilities of each observed response are displayed in figure 1. Approximately 45% of PROSPER coalitions had contact with their Prevention Coordinators two times per month. Another 35% of PROSPER coalitions had contact with their Prevention Coordinators approximately once every week. The remaining 25% of communities had contact with their Prevention Coordinators once per month, or less.