Understanding Canada's Physician Supply Through the Lens of Distribution, Gender, and Age
Bibliographic record
Abstract
Canada currently has more physicians than at any point in its history, yet there is a belief that there is a shortage particularly in the area of general or family physicians. This has been kept in the forefront of public discourse by the mainstream media which fuels public paranoia that the healthcare system is “not what it used to be.” Much of this debate stems from a series of policy changes made in the 1980s and early 1990s, which by the end of that decade left the impression that it was a system in peril. Government responded by expanding physician resources, which increased the physician population ratio almost 17% from 1993 to 2011, and 30% compared to 1980. This paper seeks to determine why the perception of physician shortages continues despite the record levels of total and per-capita physicians. To answer this, a critical examination of physician activity in three areas—location, gender, and age—was conducted. The results show a sharp decline in the number of hours available for direct care as a result of an ageing, and increasingly female, work force. Results also indicate that the location of physician practice—urban or rural—has an impact on the perception of physician shortage, with rural populations having access to significantly fewer physician resources. The paper concludes that, from a policy perspective, Canada must move beyond absolute numbers and ratios in the evaluation of physician resources, and instead focus on how many direct-care hours are actually available for patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".