The earliest MD-PhD programs were small and enrolled mostly men. Here, we show that since 2014 there has been a steady increase in the number of women in MD-PhD programs, the number of women reaching parity with men in 2023. This change was due to an increase in female applicants, a decrease in male applicants, and an increase in the acceptance rate for women, which had previously been lower than that for men. Data from the National MD-PhD Program Outcomes Study show that training duration has been similar for men and women, as have most choices of medical specialties and workplaces. However, women were less likely to have full-time faculty appointments, fewer had NIH grants, and those in the most recent graduation cohort at the time of the survey reported spending less time on research than men. Previously cited reasons for these differences include disproportionate childcare responsibilities, a paucity of role models, insufficient recognition, and gender bias. Institutions can and should address these obstacles, but training programs can help by preparing their graduates to succeed despite the systemic obstacles. The alternative is a persistent gender gap in the physician-scientist workforce, lost opportunities to benefit from diverse perspectives, and a diminished impact of valuable training resources.
Lawrence F. Brass, Myles H. Akabas
Usage data is cumulative from October 2024 through January 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 983 | 37 |
433 | 13 | |
Figure | 120 | 0 |
Supplemental data | 49 | 2 |
Citation downloads | 30 | 0 |
Totals | 1,615 | 52 |
Total Views | 1,667 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.