Gender Equity Issues in Orthopaedics: A Scoping Review
Bibliographic record
Abstract
Background: Patients have improved outcomes when their diversity is reflected in the healthcare team providing treatment. Despite recent increases in female representation, orthopaedics continues to lag behind other medical specialties. The purpose of this study was to identify major themes relating to gender equity in orthopaedics, examine trends in gender representation, and summarize the existing evidence within these themes. Methods: We conducted a scoping review in accordance with PRISMA guidelines using Medline, EMBASE and Global Index Medicus to identify original research articles on gender equity issues in orthopaedics. Data-driven studies examining issues across the orthopaedic training spectrum were included. A thematic analysis was conducted and descriptive statistics were used to describe trends within each theme. Results: = 133) of studies originated from the USA, and 74.4% of studies were published from 2021 to 2025. Nearly all included studies reported that while there have been modest improvements in female representation, there remains significant gender disparity, and the field lags behind other specialties. Conclusion: This scoping review identified a compelling volume of literature demonstrating that women are underrepresented across all career stages in orthopaedics. Women surgeons disproportionately experience microaggressions, discrimination and health impacts. While representation is improving, concerted, collaborative efforts are needed to achieve gender parity in the field globally. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-025-01415-4.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".