Impact of gender and engagement on scholarly collaboration at a Clinical Translational Science Awards Program Hub

Mia T. Vogel, Bobbi J. Carothers, Todd B. Combs, Edward Tsai, Douglas A. Luke


The Clinical and Translational Science Awards (CTSA) Program is a large, federally-funded, infrastructure grant which aims to support innovative solutions to the toughest problems facing the health sciences and translate them efficiently and effectively from the laboratory to clinic, community, and policy settings. In 2018, there were 58 medical research institution “hubs” receiving CTSA funding across the United States. In this network analysis, we analyze collaboration ties in a rich network dataset of CTSA program members from one such hub collected annually across 10 years (from 2008 through 2018). The purpose of these analyses is to improve our understanding of collaboration network structures of women conducting clinical and translational research and identify potential leverage points for addressing disparities. We examine the engagement (e.g., use of CTSA core services, leadership, mentoring, receipt of internal funding) and collaboration on grant submissions and peer-reviewed publications of women across disciplines and races/ethnicities in comparison to men. We report descriptive statistics and visualizations of ten annual publication and grant collaboration networks and ERGM models examining the relationship of engagement and gender on likelihood of collaboration. Women face systemic challenges in academia; they are historically underrepresented, tend to occupy lower status positions, and earn less money than their male counterparts. This research has implications for evaluating the CTSA program and strategizing targeted interventions at structural and relational levels for improving engagement, collaborative inclusion, and scholarly productivity of women in clinical and translational research. Network analyses such as the ones presented here may reveal leverage points for addressing systematic sexism. This research was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1 UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

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