Biopsy of Canada’s family physician shortage
Bibliographic record
Abstract
Family physicians provide comprehensive care for the community and are an integral part of the healthcare system. Canada is experiencing a shortage of family physicians, driven in part by overbearing expectations of family physicians, limited support and resources, antiquated physician compensation, and high clinic operating costs. An additional factor contributing to this scarcity is the shortage of medical school and family medicine residency spots, which have not kept pace with population demand. We analysed and compared data on provincial populations and numbers of physicians, residency spots and medical school seats across Canada. Family physician shortages are the highest in the territories (>55%), Quebec (21.5%) and British Columbia (17.7%). Among the provinces, Ontario, Manitoba, Saskatchewan and British Columbia have the fewest family physicians per 100 000 persons in the population. Among the provinces that offer medical education, British Columbia and Ontario have the fewest medical school seats per population, while Quebec has the most. British Columbia has the smallest medical class size and the least number of family medicine residency spots as a function of population, and one of the highest percentages of provincial residents without family doctors. Paradoxically, Quebec has a relatively large medical class size and a high number of family medicine residency spots as a function of population, but also one of the highest percentages of provincial residents without family doctors. Possible strategies to improve the current shortage include encouraging Canadian medical students and international medical graduates to consider family medicine, and reducing administrative burdens for current physicians. Other steps include creating a national data framework, understanding physician needs to guide effective policy changes, increasing seats in medical schools and family residency programmes, providing financial incentives and facilitating entry into family medicine for international medical graduates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".