Advancing team-based primary health care: a comparative analysis of policies in western Canada
Bibliographic record
Abstract
BACKGROUND: We analyzed and compared primary health care (PHC) policies in British Columbia, Alberta and Saskatchewan to understand how they inform the design and implementation of team-based primary health care service delivery. The goal was to develop policy imperatives that can advance team-based PHC in Canada. METHODS: We conducted comparative case studies (n = 3). The policy analysis included: Context review: We reviewed relevant information (2007 to 2014) from databases and websites. Policy review and comparative analysis: We compared and contrasted publically available PHC policies. Key informant interviews: Key informants (n = 30) validated narratives prepared from the comparative analysis by offering contextual information on potential policy imperatives. Advisory group and roundtable: An expert advisory group guided this work and a key stakeholder roundtable event guided prioritization of policy imperatives. RESULTS: The concept of team-based PHC varies widely across and within the three provinces. We noted policy gaps related to team configuration, leadership, scope of practice, role clarity and financing of team-based care; few policies speak explicitly to monitoring and evaluation of team-based PHC. We prioritized four policy imperatives: (1) alignment of goals and policies at different system levels; (2) investment of resources for system change; (3) compensation models for all members of the team; and (4) accountability through collaborative practice metrics. CONCLUSIONS: Policies supporting team-based PHC have been slow to emerge, lacking a systematic and coordinated approach. Greater alignment with specific consideration of financing, reimbursement, implementation mechanisms and performance monitoring could accelerate systemic transformation by removing some well-known barriers to team-based 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 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".