Trend in areas of practice of family physicians by career stage in Ontario, Canada from 2018 to 2022
Bibliographic record
Abstract
CONTEXT: The percentage of family physicians (FPs) in Ontario who practice primary care has been decreasing over time as FPs are choosing to focus some or all of their practice in more specialized areas. There is evidence that this move away from comprehensive primary care is occurring among physicians in all stages of their career. OBJECTIVE: The objective of this study is to evaluate the trend in the diversity of areas of practice among physicians in different career stages and to identify which areas of practice have the biggest increase in each career stage. DATASET: Physician billing data submitted to the Ontario Ministry of Health by FPs over the five years from April 1, 2018 and March 31, 2023. POPULATION STUDIED: All family physicians in Ontario, who submitted a billing claim during the study period. INSTRUMENT: Areas of practice were assigned based on billing data. Career stages were classified in ten-year blocks based on the number of years since graduation. OUTCOME MEASURES: The number of areas of practice per physician and the number of FPs in each area were calculated by career stage and compared over the five years. RESULTS: Overall, the percentage of FPs who did not have primary care as one of their areas of practice increased from 21.9% in 2018 to 25.2% in 2022. An increase was observed in all career stages; however, the largest increase was among physicians who were in their first ten years of practice (22.6% to 28.2%). Overall, the percentage of FPs with two or more areas of practice increased from 30.1% in 2018 to 35.1% in 2022. Over the five years palliative care had the highest percentage increase in the number of practicing FPs (41.6%), overall and in each career stage. Mental health care had the second highest (27.2%), with most of the increase occurring among early and early-mid career FPs. Addictions treatment and hospital-based care was the third highest overall (23.2% and 22.7%), with earlier career stages being responsible for most of the change. CONCLUSIONS: There is an increasing trend in the percentage of FPs who are diversifying their practices to include more than one area of focus. This change is occurring in all career stages but is concentrated in the earlier stages. This shift is a factor contributing to existing challenges in access to primary care and should be considered when designing policy solutions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| 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) | 0.005 | 0.001 |
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".