Do Incentive Payments Reward The Wrong Providers? A Study Of Primary Care Reform In Ontario, Canada
Bibliographic record
Abstract
Primary care payment reform in the US and elsewhere usually involves capitation, often combined with bonuses and incentives. In capitation systems, providing care within the practice group is needed to contain costs and ensure continuity of care, yet this is challenging in settings that allow patient choice in access to services. We used linked population-based administrative databases in Ontario, Canada, to examine a substantial payment called the "access bonus" designed to incentivize primary care access and to minimize primary care visits outside of capitation practices. We found that the access bonus flowed disproportionately to physicians outside large cities and to those whose patients made fewer primary care visits, received less after-hours care, made more emergency department visits, and had higher adjusted ambulatory costs. Our findings indicate a lack of alignment between these payments and their intended purpose. Financial incentives should be prospectively evaluated and frequently revisited to ensure relevance, alignment with system goals, efficiency, and equity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".