Family physicians partnering for system change: A multiple-case study of Ontario Health Teams in development
Bibliographic record
Abstract
Abstract Background The Ontario Health Teams (OHT) model is a form of integrated care that seeks to provide coordinated delivery of care to communities across Ontario, Canada. Primary care is positioned at heart of the Ontario Health Teams model, yet physician participation and representation has been severely challenged at planning and governance tables. Methods The purpose of this multiple case study is to examine 1) processes and structures to enable family physician participation in OHTs and 2) describe prevalent challenges to family physician participation. We conducted semi-structured interviews with stakeholders (administrators, physicians) from OHT communities and carried out an analysis of internal and external documents to contextualize interview findings. Thematic analysis was first applied within case and then between cases. Results Four OHT communities participated in this study. Thirty-nine participants (17 family physicians; 22 other stakeholders) participated in the study. Over 60 documents were provided and analyzed. The study took place between June and December 2021. Conclusions Within-case analysis found that structures and processes should be formalized and established to facilitate physician participation. Skepticism, burnout, heavy workload, and the COVID-19 pandemic were challenges to participation. Between-case analysis found that participation varied amongst cases. Face-to-face communication processes were favoured in all cases and history of collaboration facilitated relationship-building. All cases faced similar challenges to physician participation despite regional differences.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".