Exploring Context and the Factors Shaping Team-Based Primary Healthcare Policies in Three Canadian Provinces: A Comparative Analysis
Bibliographic record
Abstract
This paper discusses findings from a high-level scan of the contextual factors and actors that influenced policies on team-based primary healthcare in three Canadian provinces: British Columbia, Alberta and Saskatchewan. The team searched diverse sources (e.g., news reports, press releases, discussion papers) for contextual information relevant to primary healthcare teams. We also conducted qualitative interviews with key health system informants from the three provinces. Data from documents and interviews were analyzed qualitatively using thematic analysis. We then wrote narrative summaries highlighting pivotal policy and local system events and the influence of actors and context. Our overall findings highlight the value of reviewing the context, relationships and power dynamics, which come together and create "policy windows" at different points in time. We observed physician-centric policy processes with some recent moves to rebalance power and be inclusive of other actors and perspectives. The context review also highlighted the significant influence of changes in political leadership and prioritization in driving policies on team-based care. While this existed in different degrees in the three provinces, the push and pull of political and professional power dynamics shaped Canadian provincial policies governing team-based care. If we are to move team-based primary healthcare forward in Canada, the provinces need to review the external factors and the complex set of relationships and trade-offs that underscore the policy process.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".