Family physician count and service provision in Ontario and Alberta between 2005/06 and 2017/18: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Five million Canadians lack a family doctor or primary care team. Our goal was to examine trends over time in family physician workforce and service provision in Ontario and Alberta, with a view to informing policy discussions on primary care supply and delivery of services. METHODS: We used cross-sectional analyses in Ontario and Alberta for 2005/06, 2012/13 and 2017/18 to examine family physician provision of service days by provider demographic characteristics and geographic location. A service day was defined as 10 or more clinic visits worth $20 or more on the same calendar day. We included all active family physicians who had evidence of billing in each fiscal year analyzed. RESULTS: From 2005/06 to 2017/18, the number of family physicians increased by 35.3% in Ontario and 48.7% in Alberta; however, annual average service days per physician declined by 10.6% in Ontario and 5.9% in Alberta. The average daily patient volume remained stable in Ontario and declined in Alberta, and services per population kept pace modestly with population growth in both provinces. Rural areas had the smallest increases in physician counts and largest declines in average annual service days per physician. Physicians in both provinces who had graduated from medical school at least 30 years earlier accounted for more than one-third of the workforce in 2017/18. INTERPRETATION: Ontario and Alberta experienced rapid growth in the number of family physicians, with the largest increases among those in late career and the lowest increases in rural areas. The decline in service provision among physicians overall and in subgroups in both provinces highlights the importance of measuring activity to inform workforce planning.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".