Gender-based differences in physician payments within the fee-for-service system in Ontario: a retrospective, cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Differences in physician income by gender have been described in numerous jurisdictions, but few studies have looked at a Canadian cohort with adjustment for confounders. In this study, we aimed to understand differences in fee-for-service payments to men and women physicians in Ontario. METHODS: We conducted a cross-sectional analysis of all Ontario physicians who submitted claims to the Ontario Health Insurance Plan (OHIP) in 2017. For each physician, we gathered demographic information from the College of Physicians and Surgeons of Ontario registry. We compared differences in physician claims between men and women in the entire cohort and within each specialty using multivariable linear regressions, controlling for length of practice, specialty and practice location. RESULTS: We identified a cohort of 30 167 physicians who submitted claims to OHIP in 2017, including 17 992 men and 12 175 women. When controlling for confounding variables in a linear mixed-effects regression model, annual physician claims were $93 930 (95% confidence interval $88 434 to $99 431) higher for men than for women. Women claimed 74% as much as men when adjusting for covariates. This discrepancy was present in nearly all specialty categories. Men claimed more than women throughout their careers, with the greatest gap 10-15 years into practice. INTERPRETATION: We found a gender gap in fee-for-service claims in Ontario, with women claiming less than men overall and in nearly every specialty. Further work is required to understand the root causes of the gender pay gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".