Proportion of Female Physicians in a Specialty and Median Annual Payments in Ontario, Canada
Bibliographic record
Abstract
Importance: It is unclear why areas of medicine overrepresented by female physicians receive lower pay. One theory is devaluation-that work done by women is systematically devalued over time. Objective: To assess whether an increase in the proportion of female physicians in a specialty is associated with a decrease in the median annual payments for that specialty. Design, Setting, and Participants: In 2022, a population-based, repeated cross-sectional analysis was conducted using routinely collected data of all active physicians in Ontario, Canada, from 1992-1993 (1993) to 2019-2020 (2020). For each year, the percentage of female physicians and total annual median payments for each specialty were calculated. Main Outcome and Measure: Random-effects linear regression models were constructed to evaluate the association between the percentage of female physicians and median payments overall. Additionally, we conducted a sensitivity analysis of the subgroup of physicians with 1.0 or more full-time equivalency (FTE). Results: The physician workforce increased from 18 572 in 1993 to 31 374 in 2020 with the number of female physicians increasing from 4151 (22.3%) to 13 205 (42.1%). The mean (SD) age for all physicians in 2020 was 49.6 (12.8) years, and for female physicians in 2020 was 46.2 (11.5) years. Median annual payments increased from $183 033 to $316 570. Regression results found that an absolute 1 percentage point increase in the percentage of female physicians in a specialty over 1 year was associated with a $2183 lower increase in median payments than expected overall (95% CI, -$3932 to -$434; P = .02) and a $3235 lower increase for female physicians (95% CI, -$4888 to -$1583; P < .001), but not for male physicians (-$554; 95% CI, -$2373 to $1264). Conclusion and Relevance: This repeated cross-sectional study did not find evidence to directly support the theory of devaluation, yet the results underscore that occupational segregation, both across and within specialties, is associated with the gender pay gap. These findings should prompt medical leaders and policymakers to reconsider what activities are monetarily valued in medicine and whether these align with what patients and the public consider most valuable from a care perspective.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".