Comprehensive and Focus Practice changes in the Family Physician Workforce in Ontario:1993-2022
Bibliographic record
Abstract
Context: Comprehensive primary care is the foundation of an effective healthcare system but has been on the decline as more family physicians pursue careers in focused practice. Objective: The objective is to explore the changes in comprehensive and focus practice types over the last 30 years in Ontario, Canada. Study Design and Analysis: Using health administrative data held at ICES, we developed an algorithm to identify primary care physicians, and then described their type of practice (comprehensive, focused practice, other) based on the physician billings and number of days worked in a typical year. Setting: ICES contains linked encoded administrative data collected from April 1,1993 to March 31, 2022, in the province of Ontario, Canada Results: We identified an increase in primary care physicians from 11,103 in 1993/1994 to 17,411 in 2021/2022. From 1993 to 2022, the proportion of comprehensive primary care physicians decreased from 71.1% to 56.5%, while focused practice increased from 5.4% to 14.5%. The trend of very part time primary care physicians (i.e., physicians who work <44 days per year with <5 patients per day) followed a U-shaped distribution, starting at 20.5% in 1993/1994, dropping to a low of 15.0% in 2009/2010 and ending at 20.4% in 2021/2022. During this period, the population grew by 38.3%, comprehensive physicians increased by 24.6% and focused practice physicians increased by 317.9%. Comprehensive physicians declined from 0.74 to 0.66 per 1000 population. From 1993 to 2022, the average number of comprehensive primary care patient visits per physician steadily increased from 4,318 to a peak of 5,070 in 2010/2011, but then, gradually decreased to 4,215 in 2021/2022. The average number of patient visits for family physicians in focused practice steadily increased from 1,440 in 1993/1994 to 2,636 in 2021/2022. Conclusion: Over the past 30 years, there has been a substantial increase in the proportion of primary care physicians pursuing focused practice with a corresponding decrease in the number of comprehensive primary care physicians per capita. This is a significant factor contributing to our current crisis in access to primary care in Ontario specifically, and Canada as a whole.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".