Gender dynamics and academic rank in emergency medicine collaboration networks: A social network and gender propensity analysis

被引:0
|
作者
Walker, Laura E. [1 ]
Heaton, Heather A. [1 ]
Kohler, Katharina [2 ]
机构
[1] Mayo Clin, Rochester, MN 55905 USA
[2] Univ Cambridge, Cambridge, England
来源
关键词
Data science; Network analysis; Gender propensity; Research; Collaboration; Academic productivity;
D O I
10.1016/j.ajem.2024.06.037
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Background: Academic productivity is bolstered by collaboration, which is in turn related to connectivity between individuals. Gender disparities have been identi fied in academics in terms of both academic promotion and output. Using gender propensity and network analysis, we aimed to describe patterns of collaboration on publications in emergency medicine (EM), focusing on two Midwest academic departments. Methods: We identi fied faculty at two EM departments, their academic rank, and their publications from 2020 to 2022 and gathered information on their co-authors. Using network analysis, gender propensity and standard statistical analyses we assessed the collaboration network for differences between men and women. Results: Social network analysis of collaboration in academic emergency medicine showed no difference in the ways that men and women publish together. However, individuals with higher academic rank, regardless of gender, had more importance to the network. Men had a propensity to collaborate with men, and women with women. The rates of gender propensity for men and women fell between the gender ratios of emergency medicine (65% /35%) and the general population (50%/50%), 59.6% and 44%, respectively, suggesting a tendency toward homophily among men. Conclusion: Our study aims to use network analysis and gender propensity to identify patterns of collaboration. We found that further work in the area of network analysis application to academic productivity may be of value, with a particular focus on the role of academic rank. Our methodology may aid department leaders by using the information from local analyses to identify opportunities to support faculty members to broaden and diversify their networks. Crown Copyright (c) 2024 Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:40 / 46
页数:7
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