Family physician remuneration schemes and specialist referrals: Quasi‐experimental evidence from Ontario, Canada
Bibliographic record
Abstract
Understanding how family physicians respond to incentives from remuneration schemes is a central theme in the literature. One understudied aspect is referrals to specialists. Although the theoretical literature has suggested that capitation increases referrals to specialists, the empirical evidence is mixed. We push forward the empirical research on this question by studying family physicians who switched from blended fee-for-service to blended capitation in Ontario, Canada. Using several health administrative databases from 2005 to 2013, we rely on inverse probability weighting with fixed-effects regression models to account for observed and unobserved differences between the switchers and nonswitchers. Switching from blended fee-for-service to blended capitation increases referrals to specialists by about 5% to 7% per annum. The cost of specialist referrals is about 7 to 9% higher in the blended capitation model relative to the blended fee-for-service. These results are generally robust to a variety of alternative model specifications and matching techniques, suggesting that they are driven partly by the incentive effect of remuneration. Policy makers need to consider the benefits of capitation payment scheme against the unintended consequences of higher referrals to specialists.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".