The impact of targeted fee increases on the pay disparity between female and male general surgeons in British Columbia
Bibliographic record
Abstract
BACKGROUND: High-level payment data provided by Doctors of BC showed a 19.7% pay disparity in annual payments between female and male general surgeons in fiscal year 2019/20, and this was previously as high as 30% in 2012/13. This study aimed to examine the impact of targeted fee increases on pay disparity by sex over time. METHODS: The top 35 fees billed by female general surgeons, representing 76.3% of total payments, were retrospectively analyzed. The pay disparity by sex was calculated for each individual fee from 2000/01 to 2019/20. RESULTS: There were notable billing differences between female and male general surgeons. Female surgeons billed breast oncology procedures, malignancy consultations and visits, and peritoneal malignancy surgical procedures in greater proportions than did their male counterparts. Male surgeons billed hemorrhoid banding and rigid proctosigmoidoscopy in greater proportions than their female counterparts. With targeted fee increases, pay disparity by sex worsened for 17 of the top 35 fees but improved for the other 18 from 2010/11 to 2019/20, to varying degrees, resulting in an overall reduction in pay disparity by sex from 23% to 15%. If across-the-board fee increases had been implemented instead of targeted fee increases, the disparity in 2019/20 would have been 19% instead of 15%. CONCLUSION: Targeted fee increases reduced pay disparity between male and female general surgeons compared with theoretical across-the-board fee increases in British Columbia from 2010/11 to 2019/20, but not uniformly; some fee increases resulted in increased disparity. Other physician groups should conduct a similar analysis and allocate future fee changes with the aim of improving rather than worsening disparity.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".