Implementing Team-Based Innovation in Primary Health Care in British Columbia
Bibliographic record
Abstract
Improving health services integration for patients with complex needs is a national priority in Canada. Health systems in all provinces grapple with the rising complexity of patients and the services they need. Team-based primary health care (PHC) models have been implemented in diverse ways to improve patients' experiences, increase the coordination of care, improve population health and reduce costs. While some provinces have more than two decades of experience with PHC teams, others such as British Colombia (BC) have made changes more recently. We conducted an in-depth analysis of 12 provincial policy documents produced since 2011 to study the evolution of interprofessional models in PHC. BC has integrated team-based care through overarching policy support and funding from the provincial government. Structural practice changes to support team-based care, such as Primary Care Networks (PCNs), were designed to address the quadruple aim, a framework designed to improve health system performance through integrated primary care. Policies have addressed the vision and goals of team-based care, but discussion of processes that support teams, such as a strategy for capitation-based funding and team composition, were non-specific. Finally, there is a significant need for a provincial strategy for continuous quality improvement and evaluation of reforms.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".