Systemic Factors Influencing the Remuneration and Utilization of the Surgical Workforce in Ontario, Canada
Bibliographic record
Abstract
Background: A salary gap between male and female physicians has been demonstrated in many healthcare systems. A fee-for-service system, however, should theoretically be free of biases that lead to unequal salaries. It is unknown whether sex-based inequities in pay exist within fee-for-service models and how biases in the referral process contribute to inequities. Objective: To determine the magnitude of the sex-based pay gap in surgery and explore disparities in referrals to surgeons. Additionally, this thesis evaluates whether disparities are narrowing over time as more women enter surgery. Methods: We conducted three population based studies in Ontario, Canada using linked administrative databases housed at ICES. First, we used claims for surgical procedures submitted by surgeons (January 1, 2014-December 31, 2016) to compare earnings per hour spent operating to determine whether a sex-based pay gap exists in the fee-for-service system. Second, we analyzed referrals to surgeons over 20 years to examine the existence of and drivers for sex-based inequities in referrals and the influence of physician choice on disparities. Third, we compared the number of referrals to male and female surgeons across the length of their careers to examine whether disparities improved as female surgeons acquired experience, and examined temporal patterns to determine whether inequities were narrowing over time. Results: Within Ontario’s fee-for-service system, female surgeons earned 24% less per hour spent operating than male surgeons and more commonly performed procedures with the lowest hourly earnings. Male physicians demonstrated preferences for referrals to male surgeons, even after adjustment for patient and surgeon characteristics. Disparities did not narrow over time and existed across all levels of experience of female surgeons. Conclusions: Despite assumptions that a fee-for-service system can close the sex-based pay gap and that increasing entry of women into medicine will naturally correct existing inequities, this thesis demonstrates this not to be the case. Female physicians do not receive equal pay for equal time spent working and systemic disparities, such as referral bias, continue to propagate sex-based inequities in medicine. These inequities can have significant financial and career development consequences for women in medicine and require focused efforts for their mitigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".