Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: Eval Program Plann. 2022 Apr 20;92:102090. doi: 10.1016/j.evalprogplan.2022.102090

Initial conditions and functioning over time among community coalitions

Louis D Brown a,*, Rebecca Wells b, Sarah Meyer Chilenski c
PMCID: PMC9340962  NIHMSID: NIHMS1824094  PMID: 35462341

Abstract

Developing operationally strong community coalitions is critical to actualizing their potential for public health improvement. The purpose of this study was to measure how substance use prevention coalitions in Mexico functioned across their first 1.5 years, and to test associations between initial community contextual factors and subsequent coalition functioning and outcomes. Members of 19 coalitions participated in three waves of surveys about coalition context and functioning. We used paired t-tests to assess changes in coalition functioning and outcomes. Regression models estimated associations between coalition functioning and outcomes and initial community context. Among coalition functioning factors, over coalitions’ first 1.5 years, member engagement increased, as did coordinator skill and participatory leadership style. Two initial community context factors - community support for prevention and community champions - predicted several measures of process competence, but only community champions predicted perceived community improvement. Thus, community champions may play a pivotal role in later coalition success. The observed increases in member engagement and process competence may support subsequent coalition sustainability, a crucial component to realizing their potential impact on public health.

Keywords: Community coalitions, Substance abuse prevention coalitions, Coalition capacity, Coalition functioning, Readiness, Sustainability

1. Introduction

Coalitions have become a common strategy to build community capacity for public health improvement (Anderson et al., 2015). Engaging representatives of multiple sectors is intended to build residents’, service providers’, and other decision-makers’ ability to sustain positive changes even after funding for a specific projects ends (Johnson, Collins, Shamblen, Kenworthy, & Wandersman, 2017). Involving diverse stakeholders can also improve outcomes across racial and ethnic groups in the community, and across many outcomes (Arista, Vue, Byun, Choi, & Chin, 2017; Brown, Wells, Jones, & Chilenski, 2017).

Although coalitions are popular and promising, our understanding of the conditions necessary for them to succeed is limited (McNeish, Rigg, Tran, & Hodges, 2019). In particular, there is little evidence about such contextual factors as a general sense of community, community support for the coalition’s mission, and local champions for the coalition, despite research suggesting that such factors affect coalition success (Chilenski, Greenberg, & Feinberg, 2007; Domlyn & Wandersman, 2019; Fagan, Brooke-Weiss, Cady, & Hawkins, 2009; Feinberg, Chilenski, Greenberg, Spoth, & Redmond, 2007; Flewelling & Hanley, 2016; Greenberg et al., 2014; Powell & Peterson, 2014). Further, we know little about aspects of coalition functioning such as member engagement, inclusive communication, cohesion, and efficiency, despite evidence that they may also affect coalition effectiveness (Alexander et al., 2003; Cicognani, Albanesi, Valletta, & Prati, 2020; Florin, Mitchell, Stevenson, & Klein, 2000; Powell & Peterson, 2014). Similarly, we have limited evidence about how outcomes evolve over time, such as community support for the coalition and how much coalition efforts contribute to local prevention-related capacity and residents’ well-being (Brown, Feinberg, Shapiro, & Greenberg, 2015; Chilenski et al., 2016; Doll et al., 2012). The current study helps to fill these gaps by providing insight into the contextual factors and the initial development of several key aspects of coalition functioning necessary to support community coalition success.

Member engagement is fundamental to functioning for voluntary organizations such as community coalitions. Coalition members’ levels of participation have been positively associated with subsequent coalition capacity and outcomes, including expertize and planning, as well as coalition funding and perceived system impact (Feinberg, Greenberg, & Osgood, 2004; Greenberg et al., 2015; Perkins et al., 2011).

In addition to member engagement, coalitions’ process competencies have also supported success over time. Member reports of participatory leadership styles have been positively correlated with member engagement (Metzger, Alexander, & Weiner, 2005), use of evidence-based practices (Brown, Feinberg, & Greenberg, 2010), and sustainability (Welsh, Chilenski, Johnson, Greenberg, & Spoth, 2016). Quality of communication among coalition members has been positively associated with their confidence about coalition public health impact (Rogers et al., 1993). This suggests that coordinator skill in facilitating communication is important. In turn, groups with strong cohesion, or sense of belonging, tend to perform better (Brown et al., 2010).

Efficient coalition processes also appear to improve performance, as they are linked to increases in coalition support for evidence-based program implementation (Brown et al., 2010). A large study of coalitions also found internal organizational structure to be associated with community decreases in substance use (Flewelling & Hanley, 2016).

Coalitions’ formation and functioning may also reflect community context (Greenberg, Feinberg, Meyer-Chilenski, Spoth, & Redmond, 2007). A positive sense of community can increase local capacity for collective action. Conversely, histories of distrust can undermine partnerships (Butterfoss, 2007). Community awareness and support for public health issues can also affect coalitions’ ability to build capacity (Butterfoss, 2007). Finally, coalitions often rely on champions to marshal needed resources. Domlyn & Wandersman (2019, p. 883) define champions as “an important person(s) that supports the policy, practice, or program,” and note that they may be based in various sectors of the community, such as in local government, health services, police, and the schools, as well as parent and youth groups and unaffiliated residents. These individuals could be paid or unpaid and may or may not have had experience with substance abuse. Prior studies have found local champions to catalyze community coalitions (Butterfoss, 2007; Feinberg et al., 2007), including supporting program adaptation and sustainability (Aitaoto, Tsark, & Braun, 2009; Greenberg et al., 2015).

In the current study, we examined initial community conditions as well as changes in coalition functioning and outcomes over time in a network of community-based substance use prevention coalitions called the Red de Coaliciones Comunitarias de Mexico. Specifically, we sought to answer the following research questions: (1) How did coalitions’ functioning and outcomes change over their first 1.5 years? and (2) How did initial community context affect subsequent coalition functioning and outcomes? We hypothesized that coalitions would improve their member engagement, process competence, and the outcomes of community support for their work and community improvement attributed to coalition activities over time due to the technical assistance they all received (Brown, Hawkins, Arthur, Briney, & Fagan, 2011; Chilenski, Ang, Greenberg, Feinberg, & Spoth, 2014). Based on limited prior evidence, we did not hypothesize about the relative rates of different aspects of coalition development. However, testing each factor at multiple time points allowed the results to indicate varying speeds of evolution across these components of coalition capacity. The intent was to provide community leaders with nuanced information about how coalitions develop over time. Additionally, we hypothesized that the initial community contextual factors sense of community, community support for prevention, and community champions would be associated with subsequent coalition member engagement, process competence, community support for the coalition, and community improvement attributed to coalition activities (Basic, 2015). The intent was to identify preconditions of coalition success that might inform actions to take even before forming new coalitions.

2. Method

2.1. Participating coalitions

The Mexican coalitions examined in this study were supported through a partnership between Programa Compañeros Asociación Civil and the Alliance of Border Collaboratives, with financial support from the United States Department of Anti-Narcotic Affairs at the US Embassy in Mexico. The partnership started in 2012 with 9 coalitions in 4 cities. In 2014, 10 new coalitions in 7 cities were funded. The 19 coalitions operated in a total of 11 cities, with populations in 2010 ranging from approximately 80,000–1.6 million people (INEGI, 2010).

Local coalition coordinators were hired and trained to facilitate operations. Coalitions followed the Strategic Prevention Framework, a cyclical 5-step process of assessment, capacity building, intervention planning, implementation, and evaluation to prevent youth substance use (Orwin, Edwards, Buchanan, Flewelling, & Landy, 2012). The Community Anti-Drug Coalitions of America (CADCA) provided training on how to identify strategies addressing the fundamental causes and local conditions behind the community problems prioritized in the coalition’s community diagnosis (Yang, Foster-Fishman, Collins, & Ahn, 2012).

To strengthen coalition functioning, technical assistance providers from the Alliance of Border Collaboratives implemented the Coalition Check-Up, which uses an audit and feedback approach to inform a continuous quality improvement cycle (Ivers et al., 2014). Coalition members completed a validated assessment of coalition functioning upon coalition formation, and then again.5 and 1.5 years later. Technical assistance providers reviewed feedback reports with each coalition to celebrate strengths and prioritize weaknesses. Once priorities were set, technical assistance providers used a structured action planning process to help coalition members establish consensus on how to address prioritized weaknesses. To support action planning, technical assistance providers drew upon a guidebook reviewing evidence-informed-practices for improving each dimension of coalition functioning in the survey. Action plan implementation efforts were then evaluated in each successive administration of the survey.

Coalitions pursued strategies that primarily relied on volunteer time, in-kind, and limited cash donations. One common approach was to mobilize residents to renovate neighborhood parks frequented by drug dealers, thereby creating safe and attractive recreational spaces for families. Coalition strategies also included organizing youth events and sports competitions featuring anti-drug messages, hosting workshops, delivering health education presentations, and creating public service announcements.

2.2. Procedure

Data for the current study come from the previously described Coalition Check-Up assessments, which assess coalition functioning. Surveys were translated into Spanish and then back translated into English to identify problematic wording, which was addressed through discussion among team members fluent in both languages (Cantor et al., 2005). Surveys were then administered to coalition members by technical assistance providers via paper and pencil at coalition trainings, to which all coalition members had been invited. Surveys included a letter about the study and the voluntary nature of participation. The first survey assessed coalition context and capacity. It was administered at the initial coalition training, as each coalition was beginning to form (a total of 445 respondents across 19 coalitions). Everyone who attended the initial training completed the survey, but membership rosters for the coalitions were not developed until after the initial training, thus a response rate is not available. For later waves of data collection, we estimated survey response rates on the basis of coalition membership rosters, with individuals considered active if they attended 2 or more coalition meetings in the past 12 months. These rosters were smaller than the initial training turnout, as participants decided whether to continue their involvement.

After the initial assessment, a coalition functioning survey was administered at.5 years to 253 respondents out of 363 coalition members, for a 70% response rate (n per coalition = 5–30). The same survey was administered again at 1.5 years to 177 respondents out of 354 coalition members, for a response rate of 50% (n per coalition = 3–24). Non-respondents did not respond to survey invitations from coalition coordinators and did not attend coalition meetings where surveys were completed. Across waves, a total of 875 surveys were completed. The coalition context and capacity assessment from wave 1 was not readministered at waves 2 and 3. Member responses were aggregated to the coalition level for analysis, providing a sample size of 19 coalitions in each wave. All study procedures were approved by the primary author’s institutional review board.

2.3. Measures

Measures of community context and coalition functioning were based on previously validated scales (Brown, Chilinski, Ramos, Gallegos, & Feinberg, 2016; Brown, Feinberg, & Greenberg, 2012; Brown, Redelfs, Taylor, & Messer, 2015). Three dimensions of community context were measured when coalitions formed (wave 1): sense of community, community support for prevention, and the presence of community champions.

At waves 2 and 3, we used scales from three domains of coalition functioning: member engagement, process competence, and outcomes. Self-reported role involvement and time invested measured member engagement. Within process competence, the four measures were member perceptions of participatory leadership style, coordinator skill, cohesion, and efficiency. The outcome measures used were member perceptions of external coalition conditions: community support for the coalition and community improvement attributed to coalition activities. Along with community context and coalition functioning, we tested for possible effects of coalition size-–using rosters of active coalition members as described in the procedure section.

Reliability Within-Group (RWG) statistics of 0.64–0.95 for all scales except community champions suggested high levels of within-coalition agreement on member perceptions (James, Demare, & Wolf, 1993). An RWG value of.36 for community champions suggests distinct knowledge of influence across the different sectors represented within the coalition. Hence, this diversity of perspectives was considered valid, and appropriate for aggregating to the coalition level.

2.3.1. Community context

Sense of community (4 items, α = 0.70, RWG =0.64) involves emotional attachment to the place and the people, commitment to addressing community issues, and a perception that the community can meet their needs (e.g., “Most people in your area feel a strong tie to the community”). Response options ranged from (1) Strongly disagree to (7) Strongly agree. Community support for prevention (3 items, α = 0.41, RWG =0.81) measured local recognition of substance use as an important problem, and commitment to prevention (e.g., “Leaders of the community are committed to reducing substance use”). The scale ranged from 1 to 5, with response options that varied across items. The Cronbach’s alpha of.41 is due to a small number of diverse items, used here because their breadth was relevant. Community champions (15 items, α = 0.95, RWG =0.36) was constructed as the mean response on a 7-point Likert-like response scale, from (1) No champion to (7) A very effective champion, relative to the prompt “Is there an enthusiastic champion* for substance abuse prevention—someone who is influential and effective in promoting substance abuse prevention in their own organization and in the community, and in the following,” for each of 15 different sectors, including local government and schools. The asterisk referred to a further definition of community champion as “… anyone - a public official, a community leader, a concerned citizen, health worker, a volunteer–that works effectively to initiate and/or support an initiative.”

2.3.2. Member engagement

Role involvement (3 items, α = 0.58, RWG =0.95, Range 0–3) summed the number of unique roles adopted by coalition members by asking “What kind of roles have you played in the past 6 months in your local coalition? In the past 6 months, did you… Talk at meetings; Serve as a member of a committee; Chair/lead a committee or sub-group.” Response options were 0) No and 1) Yes for each role. Time invested (2 items, r = 0.55, RWG =0.85, Range 0–160) captured the number of hours members spent on coalition-related tasks in an average month. “During the past 6 months about how many hours, in an average month, have you given to your coalition carrying out the following activities: Meetings including both full coalition meetings and subcommittee meetings; Work outside of coalition meetings.”

2.3.3. Process competence

Participatory leadership style (3 items, α = 0.60, RWG =0.77) assessed leaders’ involvement of multiple stakeholders. E.g. “The coalition leadership…Intentionally seeks out your views.” Response options ranged from 1) Strongly disagree to 7) Strongly agree. Coordinator skill (4 items, α = 0.90, RWG =0.72) assessed perceptions of whether the lead staff person was knowledgeable and enthusiastic, possessing strong organizational and interpersonal skills (e.g., “How skilled is your coalition coordinator or lead staff person in the following areas…Interpersonal and communication skills”). Response options ranged from 1) Needs work to 7) Very strong. Cohesion (3 items, α = 0.73, RWG =0.76) measured feelings of unity, group spirit, trust, and belonging in coalitions (e.g., “There is a strong feeling of belonging in this coalition”). Response options ranged from 1) Strongly disagree to 7) Strongly agree. Coalition efficiency (3 items, α = 0.87, RWG =0.84) quantified the work ethic, efficiency, and task focus of the coalition members (e.g., “This is a highly efficient work-oriented team”). Response options ranged from 1) Strongly disagree to 7) Strongly agree.

2.3.4. Outcomes

Community support for the coalition (4 items, α = 0.84, RWG =0.68) related to key entities’ support of coalition efforts (e.g., “Does the administrative leadership in participating community agencies support your coalition’s initiatives?”). Response options ranged from 1) No to 7) A great deal. Community improvement (8 items, α = 0.89, RWG =0.85) measured perceptions of how much coalition efforts contributed to changes in areas such as community awareness, systematic prevention planning, collaboration, and the well-being of community residents (e.g., “Please indicate how each of the following areas has changed over the last year due to your coalition… Well-being of people in our community”). Response options ranged from 1) Much worse to 7) Much better.

2.4. Analysis

We used SAS version 9.4 to conduct analyses. Analyses were at the coalition level rather than multi-level because our conceptual focus was on group processes of the coalition as a whole (e.g., social cohesion) and their collective outcomes (e.g., how much the community supports the coalition). As noted above, we therefore used all available data through the mean of individual responses to survey items within each coalition as a measure of central tendency.

To test the first hypothesis, paired t-tests compared values at.5 and 1.5 years for each measure of coalition functioning. For the second hypothesis, multiple regression coefficients were used to test associations between coalition context and subsequent coalition functioning, holding coalition size constant. Due to the limited sample size, we averaged coalition functioning at.5 years and 1.5 years into a single value for use as the dependent variable. This served to minimize the number of analyses, thereby reducing the probability of type II error. Additional analyses were then conducted separately for both.5 years and 1.5 years for each association found statistically significant when averaged across time points.

In each regression model, we looked for influential observations with DFFITS (difference in fits) values greater than 0.80 (Chen, Ender, Mitchell, & Wells, 2003). In most instances, removing these observations from models did not substantially change the magnitude of estimates and we concluded these observations were true possibilities, keeping them in the final models. However, in two regression models, influential observations did meaningfully alter the regression estimates, which we report on in the results. Community champions was the only measure that had an outlier. We reduced its influence by assigning it a value three standard deviations above the mean (Tabachnick & Fidell, 2013). All models passed tests of regression assumptions, including normality of residuals with the Shapiro-Wilk W test, heteroscedasticity with the White test, multicollinearity with the variance inflation factor, nonlinearity by examining scatter plots of residual and predicted values, and model specification with the link test (Chen et al., 2003).

3. Results

3.1. Descriptive statistics

Coalitions averaged 11 members from 5 different community sectors (Table 1). The most common community sectors represented were health and independent volunteer family members. Respondents at. 5 years were 66% female and 34% male, with an average age of 40. Their educational attainment was 11% elementary, 39% middle school, 8% high school, 18% technical degree, 18% bachelor’s degree, and 5% graduate degree.

Table 1.

Coalitions’ descriptive statistics at.5 years (except for initial conditions assessed at baseline).

Variable Mean Standard
Deviation		 Minimum Maximum
Coalition size (number of members) 11.21 5.24 6 27
Number of sectors 5.17 1.52 3 10
Number of sector representatives:
 Independent volunteer family members 2.53 3.08 0 11
 Health 2.37 2.54 0 9
 Independent volunteer youth 1.63 1.92 0 6
 Nongovernmental substance use organizations 1.53 1.31 0 5
 Civic or volunteer groups 1.11 1.29 0 4
 Business 0.89 1.15 0 4
 Criminal justice 0.74 0.81 0 2
 Other governmental agencies 0.58 1.30 0 5
 Parents’ associations 0.58 0.77 0 2
 Religion 0.42 0.84 0 3
 Civic organizations for youth 0.42 0.69 0 2
 Education 0.42 0.61 0 2
 Art and culture 0.05 0.23 0 1
Initial conditions:
 Sense of community 4.04 0.31 3.48 4.48
 Community support for prevention 3.12 0.27 2.67 3.75
 Community champions 3.85 0.64 2.82 5.70
Member engagement:
 Role involvement 1.49 0.36 0.88 2.10
 Time invested 29.55 11.41 12.50 50.06
Process competence:
 Participatory leadership style 5.73 0.44 5.10 6.60
 Coordinator skill 5.58 0.81 3.45 7.00
 Cohesion 5.93 0.50 4.93 6.78
 Efficiency 6.23 0.44 5.38 6.87
Outcomes:
 Community support for coalition 5.34 0.88 2.58 6.48
 Community improvement 5.23 0.43 4.59 6.21
Open in a new tab

3.2. Coalition functioning over time

Results were partially consistent with our first hypothesis that member engagement, process competence, and outcomes would improve over time (Table 2). Measures of member engagement increased from.5 years to 1.5 years (role involvement Cohen’s d =0.75, p < .05; time invested Cohen’s d =0.74, p < .05). Additionally, two of the four measures of process competence increased (participatory leadership style Cohen’s d =0.97, p < .05; coordinator skill Cohen’s d =0.80, p < .05). Outcomes improved from.5 years to 1.5 years, but the increases were not statistically significant.

Table 2.

Changes in coalition functioning from .5 years to 1.5 years (n = 19).

Construct name .5 yrs
Mean (SD)		 1.5 yrs
Mean (SD)		 .5 to 1.5 yrs change
(Cohen’s d1)
Member engagement:
 Role involvement 1.49 (0.36) 1.73 (0.27) .24 * (0.75)
 Time Invested 29.55 (11.41) 45.19 (27.71) 15.64 * (0.74)
Process Competence:
 Participatory leadership 5.73 (0.44) 6.17 (0.47) .44 * (0.97)
 Coordinator skill 5.58 (0.81) 6.14 (0.56) .56 * (0.80)
 Cohesion 5.93 (0.50) 6.17 (0.55) .24
 Efficiency 6.23 (0.44) 6.18 (0.51) −0.05
Outcomes:
 Community support for coalition 5.34 (0.88) 5.51 (0.73) .17
 Community improvement 5.23 (0.43) 5.54 (0.60) .31
Open in a new tab

Notes:

*

p < .05

1

Cohen’s d effect size computed only for significant p < .05 findings.

3.3. Initial context predicting coalition functioning

Our second hypothesis, that initial contextual factors (sense of community, community support for prevention, and community champions) would be associated with subsequent levels of member engagement, process competence, and outcomes, was partially supported (see Table 3). Baseline sense of community was not significantly associated with any measure of subsequent coalition functioning. Community support for prevention was positively associated with three of the four process competence measures: participatory leadership style (β = 0.69, 95% CI.26 to 1.13, p < .05), coordinator skill (β = 0.73, 95% CI.30 to. 1.15, p < .05), and efficiency (β = 0.60, 95% CI.13 to 1.08, p < .05). Additionally, after excluding 3 influential observations, the relation between community support for prevention and community support for the coalition also became significant (β = 0.47, 95% CI. 15 to.78, p < .05). Follow-up analyses (Table 4) indicated associations between community support for prevention and process competence was larger with coalition functioning variables at.5 years, compared to 1.5 years. Specifically, for participatory leadership style, the standardized β estimate decreased from.54 at.5 years to.43 at 1.5 years. Similarly, for coordinator skill, the standardized β estimate decreased from.83 at.5 years to β = 0.33 at 1.5 years and efficiency decreased from β = 0.62 at.5 years to β = 0.38 at 1.5 years.

Table 3.

Regression models using measures of community context at baseline to predict coalition functioning averaged across.5 and 1.5 years (n = 19).

Coalition Functioning
Dependent Variable		 Sense of
Community		 Community
Support for
Prevention		 Community
Champions
β 95% CI β 95% CI β 95% CI
Member Engagement:
 Role Involvement .05 −0.48 to. 59 .29 −0.26 to.84 .05 −0.54 to.64
 Time Invested .06 −0.46 to.58 .41 −0.11 to.92 .04 −0.54 to.61
Process Competence:
 Participatory Leadership Style .17 −0.35 to. 69 .69 * .26 to1.13 .59 * .09 to1.08
 Coordinator Skill −0.01 −0.55 to. 5 2 .73 * .30 to1.15 .65 * .17 to1.13
 Cohesion −0.34 −0.84 to. 16 .37 −0.17 to.91 .18 −0.41 to.76
 Efficiency .01 −0.52 to. 54 .60 .13 to1.08 .41 −0.14 to.96
Outcomes:
 Community Support for Coalition .26 −0.26 to. 77 .17 −0.39 to.74 .57 .07 to1.08
 Community Improvement −0.11 −0.60 to. 3 8 .10 −0.43 to.64 .52 .05 to1.00
Open in a new tab

Note:

*

p < .05.

Table 4.

Follow-up1 regression analyses examining contextual factors at baseline as predictors of subsequent coalition functioning at.5 years and 1.5 years after baseline (n = 19).

Dependent Variable Community Support for Prevention
(DV at.5 years)		 Community Support for Prevention
(DV at 1.5 years)		 Community Champions
(DV at.5 years)		 Community Champions
(DV at 1.5 years)
B 95% CI β 95% CI β 95% CI β 95% CI
Process Competence:
 Participatory Leadership Style .54 * .05 to 1.03 .43 −0.07 to.92 .27 −0.30 to.84 .55 * .07 to 1.03
 Coordinator Skill .83 * .46 to 1.20 .33 −0.22 to.87 .53 * .02 to 1.05 .51 −0.02 to 1.03
 Efficiency .62 * .15 to 1.09 .38 −0.16 to.91 – –
Outcomes:
 Community Support – – .28 −0.28 to.85 .59 * .11 to 1.06
 Community Improvement – – .26 −0.23 to.74 .40 −0.15 to.95
Open in a new tab

Notes:

*

p < .05

1

Follow-up analyses only conducted when a contextual factor significantly predicted a measure of coalition functioning averaged across.5 and 1.5 years (see Table 3).

In Table 3, community champions at baseline were positively associated with two of the four subsequent process competence measures – participatory leadership style (β = 0.59, 95% CI.09 to 1.08, p < .05) and coordinator skill (β = 0.65, 95% CI.17 to 1.13, p < .05). Time point specific follow-up analyses (see Table 4) indicated community champions did not significantly predict participatory leadership style at.5 years (β = 0.27, 95% CI −0.30 to.84, p = .32), but did at 1.5 years (β = 0.55, 95% CI.07 to 1.03, p < .05). Community champions prediction of coordinator skill was similar at.5 and 1.5 years (β = 0.53, 95% CI.02 to 1.05, p < .05 at.5 years; β = 0.51, 95% CI −0.02 to 1.03, p = .06 at 1.5 years). Community champions predicted higher levels of both outcome measures, community support for the coalition (β = 0.57, 95% CI.07 to 1.08, p < .05) and community improvement (β = 0.52, 95% CI.05 to 1.00, p < .05). After excluding two coalitions with high scores on community champions and community improvement, the significant relation between those measures decreased to β = 0.01 (95% CI −0.62 to.63, p = .98). Time point specific analyses indicated that community champions did not significantly predict community improvement separately at.5 or 1.5 years. Community champions did not predict increases in community support for the coalition at.5 years (β = 0.28, 95% CI −0.28 to.85, p = .30), but did at 1.5 years (β = 0.59, 95% CI. 11 to p < .05).

4. Discussion

The coalition improvements in member engagement and process competence found in this sample are encouraging and may reflect capacity to create community change. The increasing member engagement between. 5 years and 1.5 years implies momentum, providing the coalition with the human resources necessary to undertake community improvement activities. The increased leader proficiency in inclusive coordination from. 5 to 1.5 years may have facilitated increases in member engagement. However, survey response rates were 70% at.5 years and 50% at 1.5 years, which may have biased findings towards higher average levels of member engagement at 1.5 years if only the more involved members responded.

The regular technical assistance from experienced individuals that coalitions received may have played an important role in helping coalitions improve their functioning. However, without an experimental design, we are unable to identify if the technical assistance played this role. Future research should explore the role of technical assistance in supporting improvements in coalition functioning.

The improved functioning at 1.5 years may also indicate the substantial timeframe needed for coalitions to clarify goals, establish group norms, and develop stable operations that allow for longer-term goal achievement. Previous work with Communities That Care coalitions suggests it takes a year to select programs, followed by another one to four years to see changes in community collaboration and youth risk and protective factors, and then another 3–7 years to see changes in youth outcomes (Brown et al., 2014; Hawkins et al., 2009). The challenge of sustaining community coalitions long enough to improve health outcomes makes it important to understand how coalition functioning develops over time and what factors support positive functioning and consequently sustainability.

Understanding what initial conditions affect success is also crucial to informing coalition practices. This study’s findings generally validate the expectation that initial community context affects coalition functioning (Feinberg et al., 2004). However, not all contextual factors predicted subsequent functioning. Despite the logic of needing a strong sense of community to be ready to implement a collaborative prevention effort (Chilenski et al., 2007), early perceptions thereof were not associated with later ratings of member engagement, coalition process competence, or the outcomes measured in this study. It is possible that a sense of community is a necessary precondition for coalition formation, but not associated with improved functioning. Given prior research in which sense of community was one component of a larger comprehensive measure of community readiness (Chilenski et al., 2007), sense of community may operate in conjunction with other readiness factors rather than in isolation. Prior research also found community readiness moderately predicted early coalition functioning (Greenberg et al., 2007) and that its influence diminished over time (Feinberg et al., 2007).

In contrast to perceptions of a sense of community in general, initial community support for prevention was associated with several facets of subsequent coalition functioning. These findings are consistent with previous research (Feinberg et al., 2007; Greenberg et al., 2007). Coalitions in more supportive local contexts may have more capacity to use resources such as training and technical assistance, thereby improving leadership and efficiency. The positive association between initial community support for prevention and later community support for coalitions, combined with elements of improved process competence, may indicate a virtuous cycle whereby supportive contexts lead to better coalition processes, and in turn to increasing support for the coalition.

The presence of community champions as coalitions formed appeared to have generally similar effects to those of community support for prevention. These champions may have supported improving leadership effectiveness over time by helping coalition leaders through advice or intercessions on their behalf. For instance, such champions may have advocated for coalition leaders with external stakeholders. It is also possible that some of the initially identified community champions became leaders within these coalitions, increasing their effectiveness over time due to the positive reinforcement associated with their initial influence in the community. Unlike community support for prevention, the initial presence of community champions was also associated with perceived subsequent community improvement; this may have reflected their ability to help coalitions translate their strategies into outcomes.

4.1. Strengths, limitations, and future directions

Key strengths of this study include its use of a nationwide sample of Mexican coalitions. Although some parts of Mexico have recently experienced extreme levels of drug-related challenges, these are not unique to this country. The longitudinal assessment allows for examination of developmental processes of community-wide collaborations. Furthermore, the study used previously validated measures of community context and coalition functioning.

Some aspects of the study design constrain what inferences can be made from results. Only perceptual measures were available, which may not fully correspond with objectively measured coalition functioning or outcomes. For coalition processes, this is also a strength, in that members were best positioned to speak to dynamics such as how effective their leadership was and how cohesive and efficiently they worked as a group.

The generally high within-coalition agreement on measures suggests that members were reporting on commonly experienced phenomena. The one exception was the number of community champions identified. Although champions may have been under-reported, if the under-reporting was consistent across coalitions, it would not bias regression estimates.

The study also had small samples, which reflects the conceptual focus on coalitions as collective entities, rather than individual members’ experiences. Although limited statistical power may have led to under-identification of true effects, analyses yielded a number of significant associations. We also used effect sizes to assess the practical significance of patterns found. Future research with larger coalition samples can more precisely identify the conditions under which coalitions thrive in promoting coordinated community action against substance abuse.

4.2. Lessons learned

Key implications from the current study include the importance of viewing community coalition development as a process that unfolds over years rather than months. This is important knowledge for funders and technical assistance providers for community coalitions, as community coalitions are likely to benefit from a long-term and stable funding plan and technical assistance structure that understands the importance of how investing now will lead to future benefits and future returns. Coalition leaders may benefit from focusing on both their own process competencies and member engagement for the first year and a half of coalition development. Leaders may find it helpful to communicate with stakeholders about how long it takes for coalitions to develop capacity. Such shared realism may reduce frustration with the time necessary for initial processes and enhance sustained engagement.

The importance of leveraging initial community support for prevention and community champions was underscored in these analyses. Coalitions may benefit from regularly identifying their champions and conducting regular outreach to keep champions informed about the coalitions’ work. The positive associations between community support for prevention and coalition process competence are reminders of how directly coalitions draw on local resources, including commitment to public health. Thus, local leaders considering initiating new coalitions might highlight health issues before coalition formation, thereby fostering conditions conducive to coalition success.

The increases in coalition leadership capacity were particularly encouraging, as previous research suggests these strengthen coalition ability to support program implementation (Brown et al., 2015). Increases in member engagement suggest coalitions can effectively mobilize residents with continued effort. These findings complement those from earlier studies, reinforcing the importance of providing training and ongoing technical assistance for coalition leaders and members. Specifically, processes such as the Coalition Check-Up may support this virtuous cycle and need to be tested (Brown et al., 2021).

4.3. Conclusion

Although community coalitions require substantial planning and the integration of diverse perspectives, findings from this study suggest that they are perceived as becoming increasingly effective over time. By documenting the evolution of these collaborative processes and how initial conditions influence their trajectories, we can better anticipate and address coalition challenges. Technical assistance, training, and funding opportunities can be organized with this knowledge in mind to support coalition functioning and sustainability. Through the accumulation of coalition momentum, community members committed to change can succeed in making positive sustained impacts on public health.

Acknowledgments

This research was supported by a grant from the U.S. Embassy in Mexico. Additionally, this study was supported in part by the U.S. National Cancer Institute (NCI) through a Community Networks Program Center grant, U54 CA153505 and by the National Institute on Drug Abuse under award number R01DA045815. Findings and recommendations herein are not official statements of the U.S. Embassy, the NCI or NIDA. We are grateful for the time contributed by the many coalition members who participated in this study. We also acknowledge the leadership of Rebeca Ramos, Nora Gallegos, and Apolonia Hernandez of the Alliance of Border Collaboratives, without whom this research would not have been possible. We would also like to thank Aileen Soto, Denise Vasquez, and Campbell Tole for their help with data cleaning and management.

Biographies

Louis D. Brown, Ph.D. is an Associate Professor in the Department of Health Promotion and Behavioral Sciences at The University of Texas Health Science Center at Houston, School of Public Health in El Paso, Texas.

Rebecca Wells, Ph.D., MHSA is a Professor in the Department of Management, Policy, and Community Health at The University of Texas Health Science Center at Houston, School of Public Health in Houston, Texas.

Sarah Meyer Chilenski, Ph.D. is a Research Associate Professor in the Edna Bennett Pierce Prevention Research Center at The Pennsylvania State University in State College, PA.

Footnotes

CRediT authorship contribution statement

Louis D. Brown: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Supervision. Rebecca Wells: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Sarah Meyer Chilenski: Conceptualization, Methodology, Writing – original draft, Writing – review & editing.

Declarations of interest

None.

References

  1. Aitaoto N, Tsark J, & Braun KL (2009). Sustainability of the Pacific Diabetes Today Coalitions. Preventing Chronic Disease, 6(4). [PMC free article] [PubMed] [Google Scholar]
  2. Alexander JA, Weiner BJ, Metzger M, Shortell SM, Bazzoli GJ, Hasnain-Wynia R, & Conrad DA (2003). Sustainability of collaborative capacity in community health partnerships: A proposed model and empirical test. Medical Care Research & Review, 60(4), 130S–160S. [DOI] [PubMed] [Google Scholar]
  3. Anderson LM, Adeney KL, Shinn C, Safranek S, Buckner-Brown J, & Krause LK (2015). Community coalition-driven interventions to reduce health disparities among racial and ethnic minority populations. Cochrane Database of Systematic Reviews, 6, Article Cd009905. 10.1002/14651858.CD009905.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Arista P, Vue C, Byun M, Choi A, & Chin KK (2017). The RAISE coalition: Lessons learned from a multisector coalition used to implement high-impact population-wide strategies in Cleveland, Ohio. Progress in Community Health Partnerships, 11(3), 309–314. 10.1353/cpr.2017.0036 [DOI] [PubMed] [Google Scholar]
  5. Basic J (2015). Community mobilization and readiness: Planning flaws which challenge effective implementation of ‘communities that care’ (CTC) prevention system. Substance Use & Misuse, 50(8–9), 1083–1088. 10.3109/10826084.2015.1007655 [DOI] [PubMed] [Google Scholar]
  6. Brown EC, Hawkins JD, Arthur MW, Briney JS, & Fagan AA (2011). Prevention service system transformation using communities that care. Journal of Community Psychology, 39(2), 183–201. 10.1002/jcop.20426 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brown EC, Hawkins JD, Rhew IC, Shapiro VB, Abbott RD, Oesterle S, & Catalano RF (2014). Prevention system mediation of communities that care effects on youth outcomes. Prevention Science, 15(5), 623–632. 10.1007/s11121-013-0413-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Brown LD, Chilenski SM, Wells R, Jones EC, Welsh JA, Gayles JG, & Feinberg ME (2021). Protocol for a hybrid type 3 cluster randomized trial of a technical assistance system supporting coalitions and evidence-based drug prevention programs. Implementation Science, 16(1), 64. 10.1186/s13012-021-01133-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brown LD, Chilinski S, Ramos R, Gallegos N, & Feinberg ME (2016). Community prevention coalition context and capacity assessment: Comparing the United States and Mexico. Health Education & Behavior, 43, 145–155. 10.1177/1090198115596165 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brown LD, Feinberg ME, & Greenberg MT (2010). Determinants of community coalition ability to support evidence-based programs. Prevention Science, 11, 287–297. 10.1007/s11121-010-0173-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Brown LD, Feinberg ME, & Greenberg MT (2012). Measuring coalition functioning: Refining constructs through factor analysis. Health Education & Behavior, 39, 486–497. 10.1177/1090198111419655 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Brown LD, Feinberg ME, Shapiro VB, & Greenberg MT (2015). Reciprocal relations between coalition characteristics and the provision of implementation support. Prevention Science, 16, 101–109. 10.1007/s11121-013-0447-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brown LD, Redelfs AH, Taylor TJ, & Messer RL (2015). Comparing the functioning of youth and adult partnerships for health promotion. American Journal of Community Psychology, 56(1–2), 25–35. 10.1007/s10464-015-9730-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brown LD, Wells R, Jones EC, & Chilenski SM (2017). Effects of sectoral diversity on community coalition processes and outcomes. Prevention Science, 18, 600–609. 10.1007/s11121-017-0796-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Butterfoss FD (2007). Coalitions and partnerships in community health. John Wiley & Sons [Google Scholar]
  16. Cantor SB, Byrd TL, Groff JY, Reyes Y, Tortolero-Luna G, & Mullen PD (2005). The language translation process in survey research: A cost analysis. Hispanic Journal of Behavioral Sciences, 27, 364–370. 10.1177/0739986305277940 [DOI] [Google Scholar]
  17. Chen X, Ender P, Mitchell M, & Wells C (2003). Regression with SAS. 〈http://www.ats.ucla.edu/stat/sas/webbooks/reg/default.htm〉. [Google Scholar]
  18. Chilenski SM, Ang PM, Greenberg MT, Feinberg ME, & Spoth R (2014). The impact of a prevention delivery system on perceived social capital: The PROSPER Project. Prevention Science, 15(2), 125–137. 10.1007/s11121-012-0347-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chilenski SM, Greenberg MT, & Feinberg ME (2007). Community readiness as a multidimensional construct. Journal of Community Psychology, 35(3), 347–368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chilenski SM, Perkins DF, Olson J, Hoffman L, Feinberg ME, Greenberg M, & Spoth R (2016). The power of a collaborative relationship between technical assistance providers and community prevention teams: A correlational and longitudinal study. Evaluation and Program Planning, 54, 19–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Cicognani E, Albanesi C, Valletta L, & Prati G (2020). Quality of collaboration within health promotion partnerships: Impact on sense of community, empowerment, and perceived projects’ outcomes. Journal of Community Psychology, 48(2), 323–336. [DOI] [PubMed] [Google Scholar]
  22. Doll M, Harper GW, Robles-Schrader GM, Johnson J, Bangi AK, Velagaleti S, & Adolescent Medicine Trials Network for HIV/AIDS Interventions. (2012). Perspectives of community partners and researchers about factors impacting coalition functioning over time. Journal of Prevention & Intervention in the Community, 40(2), 87–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Domlyn AM, & Wandersman A (2019). Community coalition readiness for implementing something new: Using a Delphi methodology. Journal of Community Psychology, 47(4), 882–897. 10.1002/jcop.22161 [DOI] [PubMed] [Google Scholar]
  24. Fagan AA, Brooke-Weiss B, Cady R, & Hawkins JD (2009). If at first you don’t succeed. Keep trying: Strategies to enhance coalition/school partnerships to implement school-based prevention programming. Australian & New Zealand Journal of Criminology, 42(3), 387–405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Feinberg ME, Chilenski SM, Greenberg MT, Spoth R, & Redmond C (2007). Community and team member factors that influence the operations phase of local prevention teams: The PROSPER project. Prevention Science, 8, 214–226. 10.1007/s11121-007-0069-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Feinberg ME, Greenberg MT, & Osgood DW (2004). Readiness, functioning, and perceived effectiveness of community prevention coalitions: A study of communities that care. American Journal of Community Psychology, 33, 163–176. [DOI] [PubMed] [Google Scholar]
  27. Flewelling RL, & Hanley SM (2016). Assessing community coalition capacity and its association with underage drinking prevention effectiveness in the context of the SPF SIG. Prevention Science, 17(7), 830–840. [DOI] [PubMed] [Google Scholar]
  28. Florin P, Mitchell R, Stevenson J, & Klein I (2000). Predicting intermediate outcomes for prevention coalitions: A developmental perspective (pp. 341–346). Pergamon. [Google Scholar]
  29. Greenberg MT, Feinberg ME, Johnson LE, Perkins DF, Welsh JA, & Spoth RL (2014). Factors that predict financial sustainability of community coalitions: Five years of findings from the PROSPER partnership project. Prevention Science, 16(1), 158–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Greenberg MT, Feinberg ME, Johnson LE, Perkins DF, Welsh JA, & Spoth RL (2015). Factors that predict financial sustainability of community coalitions: Five years of findings from the PROSPER partnership project. Prevention Science, 16(1), 158–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Greenberg MT, Feinberg ME, Meyer-Chilenski S, Spoth R, & Redmond C (2007). Community and team member factors that influence the early phase functioning of community prevention teams: The PROSPER project. Journal of Primary Prevention, 28(6), 485–504. 10.1007/s10935-007-0116-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hawkins JD, Oesterle S, Brown EC, Arthur MW, Abbott RD, Fagan AA, & Catalano RF (2009). Results of a type 2 translational research trial to prevent adolescent drug use and delinquency: A test of communities that care. Archives of Pediatric and Adolescent Medicine, 163, 789–798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. INEGI (2010). Mexico en cifras: Informaction nacional, pro entidad federativa y municipios. 〈http://www3.inegi.org.mx/sistemas/mexicocifras/default.aspx?e=08〉.
  34. Ivers NM, Sales A, Colquhoun H, Michie S, Foy R, Francis JJ, & Grimshaw JM (2014). No more ‘business as usual’ with audit and feedback interventions: Towards an agenda for a reinvigorated intervention. Implementation Science, 9(1), 14. 10.1186/1748-5908-9-14 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. James LR, Demare RG,& Wolf G (1993). RWG: An assessment of within-group interrater agreement. Journal of Applied Psychology, 78, 306–309. [Google Scholar]
  36. Johnson K, Collins D, Shamblen S, Kenworthy T, & Wandersman A (2017). Long-term sustainability of evidence-based prevention interventions and community coalitions survival: A five and one-half year follow-up study. Prevention Science, 18 (5), 610–621. 10.1007/s11121-017-0784-2 [DOI] [PubMed] [Google Scholar]
  37. McNeish R, Rigg KK, Tran Q, & Hodges S (2019). Community-based behavioral health interventions: Developing strong community partnerships. Evaluation and Program Planning, 73, 111–115. 10.1016/j.evalprogplan.2018.12.005 [DOI] [PubMed] [Google Scholar]
  38. Metzger ME, Alexander JA, & Weiner BJ (2005). The effects of leadership and governance processes on member participation in community health coalitions. Health Education & Behavior, 32(4), 455–473. [DOI] [PubMed] [Google Scholar]
  39. Orwin RG, Edwards JM, Buchanan RM, Flewelling RL, & Landy AL (2012). Data-driven decision making in the prevention of substance-related harm: Results from the Strategic Prevention Framework State Incentive Grant Program. Contemporary Drug Problems: An Interdisciplinary Quarterly, 39, 73–106. [Google Scholar]
  40. Perkins DF, Feinberg ME, Greenberg MT, Johnson LE, Chilenski SM, Mincemoyer CC, & Spoth RL (2011). Team factors that predict to sustainability indicators for community-based prevention teams. Evaluation and Program Planning, 34(3), 283–291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Powell KG, & Peterson NA (2014). Pathways to effectiveness in substance abuse prevention: Empowering organizational characteristics of community-based coalitions. Human Service Organizations: Management, Leadership & Governance, 38 (5), 471–486. [Google Scholar]
  42. Rogers T, Howard-Pitney B, Feighery EC, Altman DG, Endres JM, & Roeseler AG (1993). Characteristics and participant perceptions of tobacco control coalitions in California. Health Education Research: Theory & Practice, 8(3), 345–357. [Google Scholar]
  43. Tabachnick BG, & Fidell LS (2013). Using multivariate statistics (6th ed.). Needham Heights, MA: Pearson. [Google Scholar]
  44. Welsh JA, Chilenski SM, Johnson L, Greenberg MT, & Spoth RL (2016). Pathways to sustainability: 8-Year follow-up from the PROSPER project. The Journal of Primary Prevention, 37(3), 263–286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yang E, Foster-Fishman P, Collins C, & Ahn S (2012). Testing a comprehensive community problem-solving framework for community coalitions. Journal of Community Psychology, 40, 681–698. 10.1002/jcop.20526 [DOI] [Google Scholar]

RESOURCES