Objective: The authors sought to determine the medical schools with the highest number and proportion of female graduates currently practicing neurosurgery and to identify medical school characteristics that increase female representation in neurosurgery, with the goal of addressing the gender disparity.
Methods: The authors used the Physician Compare National Downloadable File from the Centers for Medicare & Medicaid Services. Physicians indicating neurosurgery as their primary specialty were extracted. Duplicates, physicians with medical school education listed as "other," and those expected to be in residency, fellowship, or research years (graduation years 2014-2023) were removed. Medical schools with the highest number and proportion of female neurosurgery graduates were stratified. A review of the current literature was conducted to identify characteristics of the institutions with high female representation.
Results: A total of 3486 neurosurgeons (319 female [9.2%], 3167 male [90.8%]) were identified. Yale University (n = 12), Columbia University (n = 8), Johns Hopkins University (n = 8), Stanford University (n = 8), and the University of Maryland (n = 8) had the most female neurosurgery graduates. Schools with the highest proportion of female neurosurgery graduates included the University of California, San Diego (25%); the State University of New York Upstate Medical University (25%); Pennsylvania State University (22.2%); the University of Maryland (21.1%); and the University of Florida (18.4%).
Conclusions: Achieving gender diversity in neurosurgery necessitates a multifaceted approach. Institutions with a higher number and proportion of female neurosurgery graduates emphasized female-female mentorship, fostered diversity initiatives, and implemented inclusive policies. To increase female representation in neurosurgery, it is crucial to establish robust mentorship programs that provide aspiring female neurosurgeons with the guidance, support, and motivation required to navigate a traditionally male-dominated field.
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