Gender Disparity in Academic Neurosurgery
Bibliographic record
Abstract
Background In the 1960s, less than 10% of medical school graduates were women. Today, almost half of all medical school graduates are women. Despite the significant rise in female medical school graduates, there continues to be a large gender gap in most subspecialties, particularly surgical subspecialties such as neurosurgery. Objective The purpose of our study was to assess the factors contributing to differences in the academic ranks of male and female staff in academic neurosurgery programs in Canada and the United States (US). Methods Data about women in academic neurosurgery was collected from a number of sources, including Fellowship and Residency Electronic Interactive Database (FREIDA), Accreditation Council for Graduate Medical Education (ACGME), Canadian Resident Matching Service (CaRMS) FRIEDA, ACGME, CaRMS, Pubmed, and Scopus, to create a database of all neurosurgeons in the US and Canada. The analysis included neurosurgeons in academic and leadership ranks and also the H index, citations, publications, citations per year, and publications per year. Results Women represent only 12% of neurosurgeons in the US and Canada. When gender is further analyzed by academic appointment, women represent just over 12% of neurosurgeons at the assistant and associate professor levels (15.44% and 13.27%, respectively) but significantly less at the full professor level (5.84%). Likewise, only 7.45% of women hold first-in command leadership positions while 4.69% hold second-in-command positions within their institutions. Conclusions The existing data shows that women are significantly under-represented in academic neurosurgery. Lack of role models, experience, limited scientific output, and aspirations of a controlled lifestyle could be the potential contributing factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".