Disparities in Radiologist Fee-For-Service Payments by Gender in Canada
Bibliographic record
Abstract
Objective: To examine differences in fee-for-service (FFS) payments to men and women radiologists in Canada and evaluate potential contributors. Methods: Publicly available FFS radiology billing data was analyzed from British Columbia (BC), Ontario (ON), Prince-Edward Island (PEI) and Nova Scotia (NS) between 2017 and 2021. Data was analyzed by gender on a per-province and national level. Variables evaluated included year, province, procedure billings, and days worked (BC and ON only). The gender pay gap was expressed as the difference in mean billing payments between men and women divided by mean payments to men. Results: Data points from 8478 radiologist years were included (2474 [29%] women and 6004 [71%] men). The unadjusted difference in annual FFS billings between men and women was $126,657. Overall, payments to women were 81% of payments to men with a 19% gender pay gap. The difference in billings between men and women did not change significantly between 2017 and 2021 (range in gender pay gap, 17–21%) but did vary by province (highest gap NS). Compared to men, women worked fewer days per year (weighted mean 218 ± 29 vs 236 ± 25 days/year, P < .001, 8% difference). Conclusion: In an analysis of fee-for-service payments to radiologists in 4 Canadian provinces between 2017 and 2021, payments to women were 81% of payments to men with a 19% gender pay gap. Payments were lower to women across all years evaluated. Women worked 8% fewer days per year on average than men, which did not fully account for the difference in FFS billing payments between men and women. Summary Statement: In an analysis of fee-for-service payments to Canadian radiologists between 2017 and 2021, payments to women were 81% of payments to men with a 19% gender pay gap which is not fully accounted for by time spent working.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".