Facilitating Integration Through Team-Based Primary Healthcare: A Cross-Case Policy Analysis of Four Canadian Provinces
Bibliographic record
Abstract
INTRODUCTION: Team-based care can improve integrated health services by increasing comprehensiveness and continuity of care in primary healthcare (PHC) settings. Collaborative models involving providers from different professions can help to achieve coordinated, high-quality person-centred care. In Canada, there has been variation in both the timing/pace of adoption and approach to interprofessional PHC (IPHC) policy. Provinces are at different stages in the development, implementation, and evaluation of team-based PHC models. This paper describes how different policies, contexts, and innovations across four Canadian provinces (British Columbia, Alberta, Ontario, Quebec) facilitate or limit integrated health services through IPHC teams. METHODS: Systematic searches identified 100 policy documents across the four provinces. Analysis was informed by Walt and Gilson's Policy Triangle (2008) and Suter et al.'s (2009) health system integration principles. Provincial policy case studies were constructed and used to complete a cross-case comparison. RESULTS: Each province implemented variations of an IPHC based model. Five key components were found that influenced IPHC and integrated health services: patient-centred care; team structures; information systems; financial management; and performance measurement. CONCLUSION: Heterogeneity of the implementation of PHC teams across Canadian provinces provides an opportunity to learn and improve interprofessional care and integrated health services across jurisdictions.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.020 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".