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The widespread adoption of science-based prevention requires local infrastructures for prevention service delivery. Communities That Care (CTC) is a tested prevention service delivery system that enables a local coalition of community stakeholders to use a science-based approach to prevention and improve the behavioral health of young people. This paper uses data from the Community Youth Development Study (CYDS), a community-randomized trial of CTC, to examine the extent to which better internal team functioning of CTC coalitions increases the community-wide adoption of science-based prevention within 12 communities, relative to 12 matched comparison communities. Specifically, this paper examines the potential of both a direct relationship between coalition functioning and the community-wide adoption of science-based prevention and a direct relationship between functioning and the coalition capacities that ultimately enable the adoption of science-based prevention. Findings indicate no evidence of a direct relationship between four dimensions of coalition functioning and the community-wide adoption of a science-based approach to prevention, but suggest a relationship between coalition functioning and coalition capacities (building new member skills and establishing external linkages with existing community organizations) that enable science-based prevention. [ABSTRACT FROM AUTHOR]

Background: Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes.Objective: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models.Data Sources: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process.Results: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors.Conclusions/implications: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients. [ABSTRACT FROM AUTHOR]