THE GENDER PAY GAP IN ORTHOPAEDIC SURGERY IS STATISTICALLY SIGNIFICANT
Bibliographic record
Abstract
Gender inequity can take many forms. The gender wage gap between females and males in orthopaedic surgery remains one of the least well understood forms of gender-based disparities. The aim of this scoping review was to evaluate the globally-available quantitative data reporting wages and payment of female and male-identifying orthopaedic surgeons. Scoping review methodology was used to conduct a literature search of five bibliographic databases and nineteen grey literature sources. Two independent assessors reviewed the publications. To be included within the scoping review, articles must have reported primary quantitative data concerning orthopaedic surgeon earnings with inclusion of a gender-based analysis. Twenty-five publications were included in the final analysis. Nineteen papers identified gender-based differences in annual orthopaedic surgeon earnings, which included both salaries and fee-for-service payments. The remaining six papers conducted gender-based analysis of industry payments to orthopaedic surgeons. Data from the annual earnings papers demonstrated females earned 40.8 & 97.5% compared to their male colleagues. Twelve of the 19 papers reported statistically significant lower annual earnings for females (p < 0.05 – 0.0005). Analysis of industry payments demonstrated female surgeons earned 0.5 – 73.7% in comparison to males. Four of six industry payment papers demonstrated statistically significant gender-based pay differences (p < 0.001 – 0.0001). When converted to dollar equivalents, female surgeons earned $0.41 – $0.98 of annual earnings and $0.05 & $0.74 of industry payments for every $1.00 earned by male surgeons. The results of this global scoping review demonstratedthe gender pay gap in orthopaedic surgery results in statistically significant lower earnings for female- compared with male-identifying surgeons. The enormity of gender-based payment differences in orthopaedic surgery demands urgent evaluation into the cause of these differences in earnings to expedite equity within the field.
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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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".