Network Analysis for Formative Evaluation of Collaborative, Team Science Research Partnerships

• Published in: Evaluation & the health professions Vol. 46; no. 4; pp. 334 - 343
• Main Authors: Liu, Grace M., Meadows, Meredith L., Wiley, Katherine T., Jurinsky, Jordan, Anglemyer, Andrew A., Wang, Lucy L., Schneider, Joseph T., Suiter, Sarah V.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.12.2023; SAGE PUBLICATIONS, INC
• Subjects: Academic disciplines; Advisory groups; Analysis; Authorship; Cancer; Co authorship; Collaboration; Communication; Cooperative Behavior; Formative Evaluation; Health disparities; Humans; Interdisciplinary Research; Network analysis; Partnerships; Research partnerships; Universities; partnership; health disparities; program evaluation; CPACHE; collaboration; network analysis; cancer research
• DOI: 10.1177/01632787231195642
• ISSN: 0163-2787; 1552-3918

Cancer health disparities persist across the cancer care continuum despite decades of effort to eliminate them. Among the strategies currently used to address these disparities are multi-institution research initiatives that engage multiple stakeholders and change efforts. Endemic to the theory of change of such programs is the idea that collaboration—across institutions, research disciplines, and academic ranks—is necessary to improve outcomes. Despite this emphasis on collaboration, however, it is not often a focus of evaluation for these programs and others like them. In this paper we describe a method for evaluating collaboration within the Meharry-Vanderbilt-Tennessee State University Cancer Partnership using network analysis. Specifically, we used network analysis of co-authorship on academic publications to visualize the growth and patterns of scientific collaboration across partnership institutions, research disciplines, and academic ranks over time. We presented the results of the network analysis to internal and external advisory groups, creating the opportunity to discuss partnership collaboration, celebrate successes, and identify opportunities for improvement. We propose that basic network analysis of existing data along with network visualizations can foster conversation and feedback and are simple and effective ways to evaluate collaboration initiatives.