Ontario’s Primary Care Reforms Have Transformed The Local Care Landscape, But A Plan Is Needed For Ongoing Improvement
Bibliographic record
Abstract
Primary care in Ontario, Canada, has undergone a series of reforms designed to improve access to care, patient and provider satisfaction, care quality, and health system efficiency and sustainability. We highlight key features of the reforms, which included patient enrollment with a primary care provider; funding for interprofessional primary care organizations; and physician reimbursement based on varying blends of fee-for-service, capitation, and pay-for-performance. With nearly 75 percent of Ontario's population now enrolled in these new models, total payments to primary care physicians increased by 32 percent between 2006 and 2010, and the proportion of Ontario primary care physicians who reported overall satisfaction with the practice of medicine rose from 76 percent in 2009 to 84 percent in 2012. However, primary care in Ontario also faces challenges. There is no meaningful performance measurement system that tracks the impact of these innovations, for example. A better system of risk adjustment is also needed in capitated plans so that groups have the incentive to take on high-need patients. Ongoing investment in these models is required despite fiscal constraints. We recommend a clearly articulated policy road map to continue the transformation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".