Primary Care Reform: Can Quebec’s Family Medicine Group Model Benefit from the Experience of Ontario’s Family Health Teams?
Bibliographic record
Abstract
Canadian politicians, decision-makers, clinicians and researchers have come to agree that reforming primary care services is a key strategy for improving healthcare system performance. However, it is only more recently that real transformative initiatives have been undertaken in different Canadian provinces. One model that offers promise for improving primary care service delivery is the family medicine group (FMG) model developed in Quebec. A FMG is a group of physicians working closely with nurses in the provision of services to enrolled patients on a non-geographic basis. The objectives of this paper are to analyze the FMG's potential as a lever for improving healthcare system performance and to discuss how it could be improved. First, we briefly review the history of primary care in Quebec. Then we present the FMG model in relation to the four key healthcare system functions identified by the World Health Organization: (a) funding, (b) generating human and technological resources, (c) providing services to individuals and communities and (d) governance. Next, we discuss possible ways of advancing primary care reform, looking particularly at the family health team (FHT) model implemented in the province of Ontario. We conclude with recommendations to inspire other initiatives aimed at transforming primary care.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".