Differences by sex in supply, payments and clinical activity of family physicians in Ontario: a retrospective population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The proportion of women entering medicine has increased in recent years, and understanding the different practice patterns of female and male family physicians (FPs) will provide important information for health workforce planning. We sought to evaluate differences by sex in the supply, payments and clinical activity among FPs in Ontario. METHODS: We conducted a cohort study using claims data from the Ontario Health Insurance Plan. We included all Ontario FPs who submitted claims from 1992 to 2018. We analyzed data using regression analyses for our outcomes of yearly number of FPs, payments, patient visits and distinct patients. RESULTS: The number of practising FPs increased from 10 370 in 1992 to 14 329 in 2018, with an annual increase of 155 female FPs and 13 male FPs. In 2018, male FPs outnumbered female FPs by 1159. Among male FPs, 32.7% worked less than 1 full-time equivalent (FTE) position, 18.1% worked 1 FTE and 49.2% worked more than 1 FTE, with little change over the 27-year study period. Among female FPs, the percentage of those who worked less than 1 FTE position decreased over time (58.6% in 1998 to 48.3% in 2015), those who worked 1 FTE was stable (22.2%-24.3%) and those who worked more than 1 FTE increased (18.7% in 1998 to 28.0% in 2017). Yearly payments were higher for male FPs than female FPs by 40%-60% overall and by 10%-20% in FPs who worked more than 1 FTE. For FPs who worked 1 FTE or less than 1 FTE, both sexes had similar payment amounts (from 2005-2018). For FPs who worked 1 FTE, female FPs were less likely to receive payments from fee-for-service after 2004, and had 550 fewer visits and 121 fewer patients annually than male FPs. INTERPRETATION: In Ontario, there are differences by sex in FP supply, payments, percentages of FTE groups, number of patient visits and number of distinct patients. Health administrators should be mindful of these differences when considering FP workforce plans to ensure a stronger primary health care system, with adequate health care delivery for the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".