Surgical sexism in Canada: structural bias in reimbursement of surgical care for women
Bibliographic record
Abstract
BACKGROUND: It is well established that female physicians in Canada are reimbursed at lower rates than their male counterparts. To explore if a similar discrepancy exists in reimbursement for care provided to female and male patients, we addressed this question: Do Canadian provincial health insurers reimburse physicians at lower rates for surgical care provided to female patients than for similar care provided to male patients? METHODS: Using a modified Delphi process, we generated a list of procedures performed on female patients, which we paired with equivalent procedures performed on male patients. We then collected data from provincial fee schedules for comparison. RESULTS: In 8 out of 11 Canadian provinces and territories studied, we found that surgeons were reimbursed at significantly lower rates (28.1% [standard deviation 11.1%]) for procedures performed on female patients than for similar procedures performed on male patients. CONCLUSION: The lower reimbursement of the surgical care of female patients than for similar care provided to male patients represents double discrimination against both female physicians and their female patients, as female providers predominate in obstetrics and gynecology. We hope our analysis will catalyze recognition and meaningful change to address this systematic inequity, which both disadvantages female physicians and threatens the quality of care for Canadian women.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".