Sticky Floor, Broken Ladder, and Glass Ceiling: Gender and Racial Trends Among Neurosurgery Residents
Bibliographic record
Abstract
Introduction Diversity and equity in academic medicine are critically important in improving healthcare standards and patient-related outcomes. Gender and racial disparities are some major challenges faced by the health system. This article reviews the gender and racial trends among residents of neurosurgery in the United States (US). Methods We retrospectively analyzed the data extracted from the Accreditation Council for Graduate Medical Education (ACGME)'s annual Data Resource Books from 2007 to 2019. ACGME cataloged gender as men and women and race/ethnicity was categorized as White/non-Hispanic, Asian or Pacific Island, Hispanic, Black/non-Hispanic, Native American/Alaskan, others, and unknown. Counts, proportions, relative, and absolute percentage changes were calculated to highlight trends in resident appointments over time and across the specialty of neurosurgery. Results The number of female residents increased steadily from 10.6% in 2007 to 19.3% in 2019; with an absolute increase of 8.74%, a relative increase of 63.9%, and a simultaneous decrease in male residents. When averaged across the nine-year study period, 51% of the study sample was White (non-Hispanic), followed by Asian/Pacific Islanders at 15.2%. The representation of Hispanics was 4.3%, Black/African Americans were 4.5%, Native Americans/Alaskans were 0.2%, and others were 8% of the total study population. Conclusion Our study concludes that gender and racial disparity persist within the neurosurgery residency training programs in the US. Concrete efforts at all academic levels are needed to provide greater support for the females and for the careers of underrepresented minority (URM) trainees to ensure their increased representation 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".