The Evolution of Primary Care Transformation Across Canada (2012-2021): A Multiple Comparative Case Study of 13 Jurisdictions
Bibliographic record
Abstract
Context: During the last decade, Canada’s provinces and territories have embarked on primary care reform toward high-performing systems. A paper entitled “Towards Primary Care Strategy” identified 12 features of high-performing primary healthcare systems. Objective: This study examines the evolution of primary care systems across Canada between 2013 and 2021. Study Design and Analysis: A multiple comparative case-study approach was used to explore changes in the 13 Canadian jurisdictions. Each case consisted of: (1) qualitative interviews with academics, provincial leaders, and healthcare professionals) and 2) a document and literature review of policies and innovations. Data for each case were thematically analyzed using the 12 features of high-performing primary care to describe each case and assess changes over time. This was followed by cross-case analyses. Setting: Canada. Population Studied: Primary care systems. Intervention/Instrument: Evolution of primary care systems. Outcome Measures: Progress in the 12 features of high-performing primary care systems. Results: We found that British Columbia, Alberta, Ontario, and Quebec have made the most significant progress toward primary care transformation in Canada, although no jurisdiction has achieved all attributes. There has been considerable progress in adopting health information technology across the country. Four jurisdictions have established a policy direction for primary care. Some jurisdictions have or are implementing collaborative primary care governance models. More jurisdictions are involved in patient enrollment and supporting quality improvement training. Experimentation with new interprofessional team models and funding arrangements continues. However, more investments are needed for wide-scale implementation of primary care governance mechanisms, interprofessional teams and funding arrangements, patient engagement, comprehensive performance measurement systems, leadership development, systematic evaluation, and building research capacity. Conclusion: The pace of primary care transformation has been slow in Canada. While there has been some progress toward high-performing systems, each jurisdiction has distinct opportunities to advance primary care to the level of best-performing countries. Further funding investments are needed by federal and provincial/territorial governments and by regional health authorities for primary care transformation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".