Did Family Physicians Who Opted into a New Payment Model Receive an Offer They Should Not Refuse? Experimental Evidence from Ontario
Bibliographic record
Abstract
It is widely believed that the traditional way of remunerating primary care physicians—namely, the fee-for-service (FFS) mechanism—generates suboptimal incentives to health care providers. The alternative payment scheme that is typically preferred involves some form of capitation. The objective of this paper is to investigate the degree to which family physicians (FPs) benefited financially after having switched from the traditional FFS mode of payment to a blended scheme involving capitation. The setting is Ontario over the period 2000–2004, during which two new payment models were implemented. We utilize a special survey of FPs that is merged with unique administrative data describing their medical practices as well as with income data drawn from their tax returns. We apply the methods of the non-experimental program evaluation literature to assess the impact of a change in remuneration scheme on FPs' income levels. Applying a battery of empirical techniques, our findings support the Ontario government's claims that adopting a blended payment model would increase the incomes of FPs. We estimate that physicians who switched remuneration schemes earned incomes that were approximately 25 percent higher, ceteris paribus.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".