The Neurosurgical Workforce in North America: A Critical Review of Gender Issues
Bibliographic record
Abstract
OBJECTIVE: The role of women in Western society has changed dramatically in the past several decades. Despite this, many gender disparities still exist for professionals in the health care sector. In neurosurgery, a disproportionately small percentage of the workforce in the United States and Canada is female. These figures are lower than most reported in other medical specialties. This review critically examines factors that may be influencing women's ability to advance in demanding subspecialties such as neurosurgery. METHODS: The literature on women in medicine, and surgery in particular, were reviewed to identify different issues facing women currently in practice in neurosurgery. In addition, the concerns of prospective trainees were examined. RESULTS: There remain many challenges for women entering neurosurgery, including unique lifestyle concerns, limited mentorship, out-dated career programs, and deep-seeded societal beliefs. Discrimination and harassment are also contributing factors. CONCLUSION: If neurosurgery is to continue to progress as a subspecialty, the issue of gender inequality needs to be scrutinized more closely. Innovative programs must be developed to meet the needs of current female faculty members and to ensure attracting the brightest individuals of both genders into a career in neurosurgery.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".