Feminization of the Rheumatology Workforce: A Longitudinal Evaluation of Patient Volumes, Practice Sizes, and Physician Remuneration
Bibliographic record
Abstract
OBJECTIVE: To compare differences in clinical activity and remuneration between male and female rheumatologists and to evaluate associations between physician gender and practice sizes and patient volume, accounting for rheumatologists' age, and calendar year effects. METHODS: We conducted a population-based study in Ontario, Canada, between 2000 to 2015 identifying all rheumatologists practicing as full-time equivalents (FTEs) or above and assessed differences in practice sizes (number of unique patients), practice volumes (number of patient visits), and remuneration (total fee-for-service billings) between male and female rheumatologists. Multivariable linear regression was used to evaluate the effects of gender on practice size and volume separately, accounting for age and year. RESULTS: The number of rheumatologists practicing at ≥ 1 FTE increased from 89 to 120 from 2000 to 2015, with the percentage of females increasing from 27.0% to 41.7%. Males had larger practice sizes and practice volumes. Remuneration was consistently higher for males (median difference of CAD $46,000-102,000 annually). Our adjusted analyses estimated that in a given year, males saw a mean of 606 (95% CI 107-1105) more patients than females did, and had 1059 (95% CI 345-1773) more patient visits. Among males and females combined, there was a small but statistically significant reduction in mean annual number of patient visits, and middle-aged rheumatologists had greater practice sizes and volumes than their younger/older counterparts. CONCLUSION: On average, female rheumatologists saw fewer patients and had fewer patient visits annually relative to males, resulting in lower earnings. Increasing feminization necessitates workforce planning to ensure that populations' needs are met.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".