Factors Impacting Primary Care Engagement in a New Approach to Integrating Care in Ontario, Canada
Bibliographic record
Abstract
Introduction: In 2019, Ontario's Ministry of Health (the Ministry) introduced Ontario Health Teams (OHTs) to provide population-based integrated healthcare. Primary care was foundational to this approach. We sought to identify factors that impacted primary care engagement during OHT formation from different perspectives. Methods: Interviews with 111 participants (administrators n = 80; primary care providers n = 17; patient family advisors = 14) from 11 OHTs were conducted following a semi-structured guide. Interviews were transcribed, coded, and thematically analyzed. Results: Participants felt that primary care engagement was an ongoing, continuous cycle. Four themes were identified: 1) 'A low rules environment': limited direction from the Ministry (system-level), 2) 'They're at different starting points': impact of local context (initiative-level); 3) 'We want primary care to be actively involved': engagement efforts made by OHTs (initiative-level); 4) 'Waiting to hear a little bit more': primary care concerns about the OHT approach (sector-level). Thirteen factors impacting primary care engagement were identified across the four themes. Discussion and Conclusion: The 13 factors influencing primary care engagement were interconnected and operated at health system, integrated care initiative, and sector levels. Future research should focus on integrated care initiatives as they mature, to address potential gaps in the involvement of primary care physicians.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".