Exploring the Influence of Interorganization Networks on the Dissemination of National Health Behavior Guidelines.

Background: Interorganization partnerships are important for the development and knowledge mobilization of national health behavior guidelines. However, little is known about how to improve the dissemination of guidelines across professional networks. Social network analysis may offer unique insight into the social structure of interorganization networks and provide guidance for how network features may be harnessed for effective dissemination. The objectives of this study were to apply social network analysis to (1) analyze the connectedness of organizations and/or subgroups within a national health behavior guideline network and (2) identify organization attributes associated with influential network positions.

Methods: Organizations involved in the development and dissemination of the Canadian 24-Hour Movement Guidelines for Adults were invited to complete an online survey to examine the connections among health-promoting organizations in Canada. Data were analyzed using UCINET Version 6. Network maps were generated for the interorganization network and its subgroups, and descriptive frequencies were calculated for demographic characteristics. Associations between organization attributes and centrality measures were calculated using Point-Biserial and Spearman rank correlations.

Results: Thirty-four organizations completed the survey and reported 228 organizational ties. Density scores for each dissemination network ranged from 1% to 5%, demonstrating the potential for constrained information sharing (ie, dissemination) between organizations. Five attributes were significantly associated with centrality measures, which included location, sector, size, resource allocation, and previous dissemination of sedentary behavior guidelines.

Conclusions: Findings demonstrate the utility of social network analysis for understanding knowledge mobilization across networks and offer guidance for how network features may be leveraged to enhance knowledge mobilization outcomes.