Priorities for primary care in Ontario, Canada: Results from a citizen dialogue
Bibliographic record
Abstract
Context: Despite universal health insurance, Canada’s primary care system is in crisis with more than one in five people having no access to primary care. Reforms should be grounded in the priorities of patients and the public but too often their voice is missing from decision-making tables. Objective: We conducted a Priorities Panel in Ontario, Canada to understand the public’s values, concerns, and recommendations for a better primary care system. This was one of five provincial priorities panels conducted as part of OurCare, a national initiative to engage the public about the future of primary care in Canada. Study Design and Analysis: 35 members of the public met for two virtual learning sessions on Zoom and for a three-day in-person session in Toronto, Ontario between November 2022 and February 2023. They spent 39 hours learning and deliberating about primary care and heard from 17 experts with diverse views. Setting or Dataset: Ontario, Canada Population Studied: Adults aged 18 years and over living in Ontario, Canada. 35 participants were randomly selected using a civic lottery process from a pool of over 1,250 volunteers who participated in the OurCare national survey. The civic lottery ensured panelists roughly represented the demographics of Ontario. Outcome Measures: Panelists wrote a Members’ Report outlining common values, key issues, and recommendations for a better primary care system. Results: Panelists reaffirmed the importance of primary care for all; equity was a central value. Key recommendations included moving away from solo fee-for-service physician practices to teambased care for all. Panelists recommended automatic rostering, a process similar to the public school system, whereby every person would automatically be registered to a local primary care team with some provision for patient choice. Panelists recommended the government legislate electronic medical record interoperability to enable patient access to their own records. They recommended increasing investment in primary care and expanding the Canada Health Act to include public coverage for pharmaceuticals, eye care, dental care and mental health. Conclusions: Members of the Ontario public strongly supported investment in primary care. Key recommendations included reorganizing care models to operate like the Canadian public school system to ensure equitable access to care for all Ontarians.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".