How to Pay Family Doctors: Why "Pay per Patient" is Better Than Fee for Service
Bibliographic record
Abstract
Physician compensation accounts for about one-fifth of all Canadian healthcare spending. But physicians’ decisions, particularly those made by primary care doctors, are the conduit for the majority of the system’s costs. The incentives physicians have to promote efficiency, therefore, affect the overall quality and value of healthcare services. We believe that a remuneration model for primary care doctors that emphasizes per-patient payments is the best way for health systems to pay its front-line doctors, although it is less applicable to specialists. Further, we believe that over time the capitation scheme could be extended so that primary care physicians would keep track of the costs of their referrals and prescribed treatments, to encourage the most appropriate and cost-effective methods of treatment and make better use of total health system resources.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.038 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".