Informing a governance model for integration of community pharmacists and family physicians and nurse practitioner-led practices and teams within Ontario Health Teams: A protocol
Bibliographic record
Abstract
BACKGROUND: The expansion of the scope of practice for community pharmacists has the potential to improve timely access to primary care. However, overlapping scopes of practice with family physicians (FPs) and nurse practitioners (NPs) have the potential for duplication of services or fragmented care. To optimize the benefits of this change, clear integrated governance mechanisms are needed to define roles, responsibilities, and accountabilities between providers. To date, there is no research on integrated governance models for independent primary care provider organizations. OBJECTIVE: To develop a governance model that will enable better integration of community pharmacists with FPs and NPs within Ontario Health Teams. METHODS: A multi-method study design will be used. A literature review will be conducted to identify innovative programs and policies between community pharmacies, FPs, and NPs. Then, relevant stakeholders will be identified and engaged in key informant interviews to exchange knowledge, identify priorities, and co-create policy and governance strategies. RESULTS: The scoping review will identify existing models, gaps, and governance approaches that support or hinder integration. Interviews will explore stakeholder experiences related to integrated governance. A conceptual integrated governance model will be developed based on these findings. DISCUSSION: This initiative aligns with the broader mission of improving healthcare services and outcomes in Ontario by establishing a governance model that enables better integration between community pharmacists, FPs, and NPs. This research will support policy and practice innovations to strengthen integrated delivery in Ontario and beyond.
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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 | 0.106 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".