Grassroots development of interprofessional primary care teams: a qualitative study in Canadian family practices
Bibliographic record
Abstract
BACKGROUND: Primary care access is universally a critical health system concern. Decades of research shows that continuous, comprehensive, team-based care promotes greater access to promotional, preventive, and therapeutic care. In Canada, health system investments in team-based primary care models have not yet supported consistent implementation and uptake across the country. As a result, some family practices have taken a grassroots approach to realising team-based care. AIM: To explore the factors and processes that support the successful grassroots development of team-based family practices. DESIGN AND SETTING: Using a qualitative multiple case study design, we investigated the experiences of Canadian family practices that engaged in self-initiated efforts to develop or transform into a team-based care model in Canada. METHOD: Case-relevant documents and interviews with practice leaders were analysed using an unconstrained approach to qualitative description. Data collection and analyses were guided by the theory of social innovation. RESULTS: Transformation processes were complex and multifaceted. Common activities across all cases were: developing business cases; obtaining funding; collaborating with provincial or regional governments, health authorities, and community members; ensuring buy-in from practice members; and securing space and human resources. These efforts supported alignment with local healthcare needs. Practice leaders uniformly declared that the change fostered positive outcomes, including improved access and attachment, more efficient workflows, and reduced emergency department visits. CONCLUSION: Those interested in promoting team-based family medicine should advocate for a balance of government investment and practice-level autonomy over development, which supports provider buy-in and community-appropriate innovation and responsiveness in care delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".