Changes and contributions to the gender pay gap in surgery in Canada: a repeated cross-sectional analysis from 1996 to 2020
Bibliographic record
Abstract
OBJECTIVES: Occupational gender segregation is a contributing factor to gender pay inequity in medicine but has not been thoroughly characterised. We assessed the historical relationship between surgeon sex, type of work and value of procedural payments. We hypothesised that female surgeons perform lower-paying procedures as a group, and that this could be seen both with broad historical overview and with focused analysis of major operative procedures in a specific year. DESIGN: We conducted repeated cross-sectional studies using public payment data from the Canadian Institute for Health Information. We calculated average payment per service by sex and service category and used linear regression to assess the association between proportion of female surgeons performing a procedure and payment value per procedure for 41 major procedures in 2019-2020. PARTICIPANTS: Surgeons in 10 Canadian jurisdictions from 1996 to 1997 (5459) to 2019-2020 (8069). RESULTS: The proportion of female surgeons increased over the study period from 10.5% (n=575) in 1996-1997 to 28.7% (n=2314) in 2019-2020. The sex gap in the average payment per service narrowed but persisted. A greater proportion of women's earnings came from non-procedural work in consultation and visits (43% for women vs 36% for men in 2019-2020) while a greater proportion of men's earnings was from procedural work in major surgery (23% for women vs 38% for men in 2019-2020). There was an inverse relationship between proportion of women performing a procedure and payment value such that for one percent increase in female proportion, the procedural payment was CAD$1.77 lower. CONCLUSIONS: Our findings suggest that women receive fewer procedural payments than men and tend to perform lower paying procedures. Reforms to referral systems and billing codes can help address root causes for the gender pay gap in surgery.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".