Projected timeline to achieve gender balance within the United States neurosurgical workforce exceeds 150 years: a National Plan and Provider Enumeration System analysis
Bibliographic record
Abstract
OBJECTIVE: Despite incremental progress in the representation and proportion of women in the field of neurosurgery, female neurosurgeons still represent an overwhelming minority of the current US physician workforce. Prior research has predicted the timeline by which the proportion of female neurosurgery residents may reach that of males, but none have used the contemporary data involving the entire US neurosurgical workforce. METHODS: The authors performed a retrospective analysis of the National Plan and Provider Enumeration System (NPPES) registry of all US neurosurgeons to determine changes in the proportions of women in neurosurgery across states, census divisions, and census regions between 2010 and 2020. A univariate linear regression was performed to assess historical growth, and then Holt-Winter forecasting was used to predict in what future year gender parity may be reached in this field. RESULTS: A majority of states, divisions, and regions have increased the proportion of female neurosurgeons from 2010. Given current growth rates, the authors found that female neurosurgeons will not reach the proportion of women in the overall medical workforce until 2177 (95% CI 2169-2186). Furthermore, they found that women in neurosurgery will not match their current proportion of the overall US population until 2267 (95% CI 2256-2279). CONCLUSIONS: Whereas many studies have focused on the overall increase of women in neurosurgery in the last decade, this one is the first to compare this growth in the context of the overall female physician workforce and the female US population. The results suggest a longer timeline for gender parity in neurosurgery than previous studies have suggested and should further catalyze the targeted recruitment of women into the field, an overhaul of current policies in place to support and develop the careers of women in neurosurgery, and increased self-reflection and behavioral change from the entire neurosurgery community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".