Processes and Structures Enabling Family Physician Participation in Ontario Health Teams: A Multiple Case Study
Bibliographic record
Abstract
Context: In 2019, the provincial government of Ontario launched a new model of health care delivery called Ontario Health Teams (OHT). OHTs seek to provide better integrated care for patients by coordinating services within a region. Primary care participation is central to the success of the OHT model. OHTs are adopting different methods to enable participation from the primary care sector. Objective: The objectives are to illustrate the processes and structures used by various OHTs to enable full participation by primary care physicians and highlight shared challenges that prevent full participation. Study Design and Analysis: We used a multiple-case study design to gain a deeper understanding of family physician participation by drawing on several instrumental cases. A cross-case analysis was completed to draw out similarities and differences across cases. A document analysis was also undertaken to validate the cross-case analysis findings. Setting or Dataset: Semi-structured interviews were conducted virtually. OHT-related documentation also comprised the dataset for this study. Population Studied: The study population is comprised of primary care physicians and stakeholders including OHT leads and committee members of OHTs. We engaged “gatekeepers” at each OHT who suggested key stakeholders to interview. We also employed snowball sampling to recruit other stakeholders to the study. Intervention/Instrument: We used an interview protocol and document analysis template. Outcome Measures: Outcome measures include example processes and/or structures for physician participation and key learnings for OHTs. Results: 39 participants participated in this study. 17 (44%) were primary care physicians and 22 (56%) were OHT leads or administrators. There were few structures and processes that enabled family physician participation, due to the COVID-19 pandemic and to the OHTs being in its early stages. Inequitable distribution of resources within regions, skepticism towards system change, and funding model limitations were system-level challenges. Lack of time, loss of revenue, intangible outcomes, and poor communication and/or consultation were also significant barriers. Conclusions: Our study demonstrates that OHTs have not been able to attain full physician participation. OHTs must address barriers and initiate authentic consultation processes to maximize primary care representation across the province.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".