Year: 2024
Type: article
Abstract: Importance: Women are underrepresented in leadership positions in cardiology. Institutional grand rounds (GR) are platforms through which faculty can gain professional exposure and access to career development opportunities. The gender diversity of invited cardiology GR speakers has not been investigated. Objective: To examine the representation of women among cardiology GR speakers in the United States. Design, Setting, and Participants: This cross-sectional study reviewed all 626 internal medicine residency training program websites in the American Medical Association's FRIEDA directory to identify publicly available records of cardiology GR. Grand rounds from September 1997 through December 2022 were included; GR with more than 1 speaker and trainee-led case conferences were excluded. Exposures: Grand rounds speaker gender. Main Outcomes and Measures: Data on GR topic focus, speaker gender, and academic rank were collected. Results: A total of 3806 GR lectures at 42 institutions were included; 799 (21%) were delivered by women and 3007 (79%) by men. The proportion of women GR speakers increased by 1% per year and increased for each topic over time with the exception of electrophysiology and basic/translational science. Women were most likely to speak on women's cardiovascular health (80 lectures [10.0%] vs 17 [0.6%] for men; P < .001). Conclusions and Relevance: Women compose about 1 in 5 cardiology GR speakers with modest growth in representation over time but remain underrepresented as GR speakers for procedural cardiology and basic/translational science topics.
Source: JAMA Cardiology
Language: English
Subfield: Gender Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Funders: Yale University, Vanderbilt University, University of Miami, University of Pennsylvania, University of Washington, University of Pittsburgh, University of Louisville, University of Rochester, Virginia Commonwealth University, West Virginia University, York University, Medical Center, University of Pittsburgh, University of California, San Diego, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases
Awards: Improving Surgical Risk Prediction and Decision Making among Patients with Cirrhosis, Understanding Patient Preferences for Surgical Management in the Setting of Cirrhosis: A Mixed-Methods Approach, Improving the Detection of Hypertrophic Cardiomyopathy Using Machine Learning Applied to Electronic Health Record Data
OpenAlex ID: W4405721522