Family Physicians in Focused Practice in Ontario, Canada: A Population-Level Study of Trends From 1993/1994 Through 2021/2022
Bibliographic record
Abstract
PURPOSE: An adequate supply of family physicians who deliver comprehensive care is critical for addressing evolving population health needs, fostering health equity, and ensuring a cost-effective health system. Little is known about current trends of family physicians choosing focused practice and concurrent changes in comprehensive family physician numbers relative to population growth. METHODS: We conducted a repeated cross-sectional population-based study using administrative data to understand sex-stratified trends in focused practice from 1993/1994 through 2021/2022 in Ontario, Canada, accounting for population growth. For each fiscal year, we identified all active family physicians and classified them by practice type, leveraging a previously published algorithm on comprehensiveness. RESULTS: The proportion of family physicians in focused practice increased from 7.7% (856/11,103) in 1993/1994 to 19.2% (3,351/17,413) in 2021/2022. The 3 most prevalent focused practice types at the end of the study period were emergency (37.0%), hospitalist (26.5%), and addiction (8.3%) medicine. A greater proportion of focused practice physicians were male (60.1%) vs female (39.9%) in 2021/2022. Over the study period, the number of family physicians increased from 104 to 118 per 100,000; however, the number of comprehensive family physicians decreased from 71 to 64 per 100,000. Of the additional 6,310 family physicians who entered the workforce, 39.5% (2,495/6,310) were in focused practice. CONCLUSIONS: Over the study period, there was a decrease in the percentage of comprehensive family physicians and a substantial increase in family physicians pursuing focused practice, particularly in emergency and hospitalist medicine. Research and policy work is needed to understand and address the complex factors driving these trends.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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".