Differences in Practice Patterns and Payments for Female and Male Dermatologists: A Canadian Population-Based Study Over 3 Decades
Bibliographic record
Abstract
Background Canada’s fee-for-service physician reimbursement system, where a set rate is provided for each service, suggests that a physician sex pay gap should not exist. However, recent evidence has questioned this presumption. Objectives To characterize trends in demographics and billing, overall and by sex, for dermatologists compared to other medical and surgical specialty groups in Ontario, Canada. Methods Using population-based data, analysis of physician billing and clinical activity from Ontario, Canada, over 27 years (1992-2018) was performed. Multilevel regression models were used to examine unadjusted and adjusted differences in payments between females and males over time, while controlling for age, distinct patients seen, patient visits, and full-time equivalent. Results A total of 22 389 physicians were included in the analyses, including 381 dermatologists. The proportion of female dermatologists increased from 32% in 1992 to 46% in 2018. Dermatologists’ median Ontario Health Insurance Plan (OHIP) payments were $415 340 (IQR: 285 630-566 580) in 1992 compared to $296 750 (IQR: 164 480-493 180) in 2018. Male dermatologists’ OHIP payments were 20% more than their female counterparts across the entire study period. After adjusting for practice volumes, there was no significant pay gap amongst female and male dermatologists ( P = .42); however, the sex pay gap remained significant for the other specialty groups ( P < .001). From 1992 to 2018, dermatologists on average saw 19% fewer distinct patients per year and 15% fewer visits per patient. Conclusions The overall sex pay gap within medical dermatology can be attributed to differences in practice patterns, whereas the sex pay gap remained significant in the other specialty groups.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".