The Case of the Vanishing Quebec Physicians: How to Improve Access to Care
Bibliographic record
Abstract
Compared to most Canadian provinces, Québec is endowed with a large and fast growing number of family doctors relative to its population. In spite of this, Québec’s family physicians and the general public regularly report that provincial residents have poor access to healthcare services. This Commentary examines this paradox, looking at what has held back past reforms to primary care and what to do about it. In 2012, there were 115 family doctors in Quebec for every 100, 000 people, compared to 100 in Ontario and 109 for Canada as a whole. But roughly 15 percent of Quebec patients report not having a regular family doctor compared to about 4 percent of Ontarians. Further, 68 percent of Quebecers reported having a “somewhat” or “very difficult” time accessing after-hours care versus 58 percent of Ontarians. Why? Quebec physicians tend to spend less time at work than their counterparts in Ontario. Quebec stands out as an exceptional case among most developed countries for the shortest physician’s work week at 34.9 hours, compared to 43 in Ontario. Younger doctors (under age 45) work even less, at 32.7 hours per week. Quebec also has fewer enrolled patients per physician, 1, 081 on average compared to 1, 539 in Ontario. These numbers are lower still for younger doctors. Over the last decade, Quebec has attempted, with little success, to improve primary-care access with the creation of family health teams. But Quebec’s Groupe de Médecine Familiale expansion has been fraught with delays and, even where groups have sprung up, they are enrolling a much lower number of patients than desired. A renewed, determined effort at primary-care reform with stronger financial incentives and commitment from governments and providers is required. Financing these teams through a mixed capitation system that encourages patient mobility, registering with family doctors and physician accountability in meeting access benchmarks should go a long way to strengthen the pursuit of better access for patients. Such a model could be extended in the future to allow primary-care givers greater budgetary control to purchase some basic services on behalf of their patients. Physician buy-in and a willingness to provide more primary care in the community as opposed to hospitals are key ingredients of a successful transition.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 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".