Abstract: Using Social Network Analysis to Assess and Improve the Implementation of Evidence-Based Alcohol and Drug Prevention Services (Society for Prevention Research 23rd Annual Meeting)

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

Schedule:
Wednesday, May 27, 2015
Capitol B (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
Luanne Rohrbach, PhD, Associate Professor, University of Southern California, Los Angeles, CA
Thomas W. Valente, PhD, Professor, University of Southern California, Los Angeles, CA
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). A major component of the strategy included placing each organization into one of eight coalitions, each mandated to implement customized plans that would focus on underage drinking. There are many studies that examine coalition networks, but little work has been done in analyzing interactions between multiple coalitions, or comparisons of inter-coalition and intra-coalition collaboration processes. This study applies social network analysis to examine how organizations in the LAC prevention services initiative interacted with each other, both within and outside of coalition boundaries.

Methods: In the first part of the study, network data is derived from two general online surveys, conducted approximately one year apart. At each wave, networks were measured by asking participants whom they currently collaborate with. The second survey also asked participants whom they would like to collaborate with. Following the surveys, in-person interviews are conducted to supplement the network data.

Results: Chi-square tests using Monte Carlo simulations found a significant relationship between collaboration network membership and the coalition boundaries created for the project in both survey 1 (p<0.001) and survey 2 (p<0.001).  In contrast, the wish-list network (i.e. organizations nominated others they would like to work with) from survey 2 was not associated with coalition boundaries  (p=0.7306).

Conclusions: While collaborative groups align closely with coalitions, wish-list collaborators differ significantly from coalition membership. This suggests that coalition boundaries are potentially acting as artificial barriers, hindering the flow of innovation and information. It is possible that organizations would want to collaborate with those outside of their coalition due to past history, geographic proximity, or non-overlapping experience. In-person interviews are currently being conducted with representatives from each coalition. These interviews should help shed light on why respondents nominated certain organizations to collaborate with, and provide a basis on how to bridge coalition boundaries.