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Effects of Sectoral Diversity on Community Coalition Processes and Outcomes.

Louis D BrownRebecca WellsEric C JonesSarah Meyer Chilenski
Published in: Prevention science : the official journal of the Society for Prevention Research (2018)
Collaboration with diverse partners is challenging but essential for the implementation of prevention programs and policies. Increased communication with partners from diverse sectors may help community coalitions overcome the challenges that diversity presents. We examined these issues empirically in a study of 17 substance use prevention coalitions in Mexico. Building on coalition and workgroup literatures, we hypothesized that sectoral diversity would improve outcomes but undermine coalition processes. Conversely, we expected uniformly positive effects from higher levels of intersectoral communication. Data are from a 2015 survey of 211 members within the 17 community coalitions. Regression models used sectoral diversity and intersectoral communication to predict coalition processes (cohesion, leader-member communication, efficiency) and outcomes (community support, community improvement, sustainability planning). Sectoral diversity was negatively associated with coalition processes and was not associated with coalition outcomes. Intersectoral communication was positively associated with two of the three measures of coalition outcomes but not associated with coalition processes. Our findings concur with those from prior research indicating that sectoral diversity may undermine coalition processes. However, more communication between sectors may facilitate the coalition outcomes of community support and sustainability planning. Skilled team leaders and participatory decision making may also help coalitions promote intersectoral communication, thereby engaging diverse community sectors to implement preventive interventions and actualize sustained public health impact.
Keyphrases
  • healthcare
  • public health
  • mental health
  • primary care
  • palliative care
  • skeletal muscle
  • machine learning
  • hepatitis c virus
  • big data