Abstract: Using Social Network Analysis to Assess and Improve the Implementation of Evidence-Based Alcohol and Drug Prevention Services (Society for Prevention Research 22nd Annual Meeting)

311 Using Social Network Analysis to Assess and Improve the Implementation of Evidence-Based Alcohol and Drug Prevention Services

Schedule:
Thursday, May 29, 2014
Yosemite (Hyatt Regency Washington)
* noted as presenting author
Kar-Hai Chu, PhD, Postdoctoral Fellow, University of Southern California, Los Angeles, CA
Elena Hoeppner, MPH, Project Manager, University of Southern California, Los Angeles, CA
Thomas W. Valente, PhD, Professor, University of Southern California, Los Angeles, CA
Luanne Rohrbach, PhD, Associate Professor, University of Southern California, Los Angeles, CA
PRESENTATION TYPE: Individual Paper

TITLE: Using Social Network Analysis to Assess and Improve the Implementation of Evidence-based Alcohol and Drug Prevention Services

Introduction: Beginning in 2011, 33 organizations joined an initiative to implement evidence-based alcohol and drug prevention services in communities throughout Los Angeles County (LAC). These agencies were required to use SAMHSA’s Strategic Prevention Framework (SPF), and placed into 1 of 8 coalition groups. Past studies on coalitions found success comes from having strong leadership, active participation, and group cohesion (Zakocs & Edwards 2006). Structural position within a coalition network has also been found to be associated with perceptions of coalition function and performance (Valente et al. 2008). However, there has been little work in analyzing interactions between multiple coalitions, or comparisons of inter-coalition and intra-coalition collaboration processes. Network theories emphasize the different advantages of bridging (weak, inter-group) and bonding (strong, intra-group) forms of ties (Putnam 2000). The purpose of the present study is to examine how organizations in the LAC prevention services initiative communicated with each other, and link bridging and bonding behaviors to coalition performances.

Methods: The data for this study comes from a larger project evaluating the organizations’ process in applying the SPF. Participants included 40 survey respondents, representing 33 provider organizations. The participants were asked to identify other organizations with whom they have collaborated. This network data is used in conjunction with each organization’s coalition membership.

Results: The network consists of 40 nodes, connected by 294 ties. It has a density of 0.188 and an average clustering coefficient of 0.255. The average degree per node is 7.35. Divided into 8 coalitions, we observed an E/I ratio of 0.24. Modularity analysis found 5 distinct clusters, and a QAP analysis reports a moderate correlation with their coalition membership (r = 0.486, p = 0.000).

Conclusions: Observations of coalition meetings inform us that groups tend to function at different levels, and display both centralized and decentralized hierarchical structures. The modularity/QAP analyses show that all coalitions focus on bonding rather than bridging behavior. However, discovering that organizations are communicating across coalition boundaries is a promising find. Surprisingly, several highly central nodes appear to serve as prominent boundary spanners, connecting with multiple coalitions. Two planned follow-ups, a coalition-focused survey and interviews with individual organizations, will help us to further refine our study.