Is Pay-for-Performance Moving North? P4P Prospects in the Canadian Healthcare System
Bibliographic record
Abstract
Recently, significant momentum has gathered behind reimbursement systems that compensate healthcare providers on the basis of the quality of care delivered (pay-for-performance, or P4P). The United States and the United Kingdom have both embraced P4P as a potential solution to many of the ills of healthcare. Every P4P system, however, must be designed to meet the needs of the local or national healthcare environment in which it is being applied. In most settings, P4P has been implemented prior to there being a full understanding of its potential effectiveness or of all the potential problems that might result from the law of unintended consequences. Each of Canada's provinces and territories has the flexibility to operate its own unique healthcare system within broad outlines put forth under the Canada Health Act of 1984. Consequently, Canada has the opportunity to contribute greatly to our knowledge base by implementing P4P in a phased-in manner that would provide the opening for both experimentation and evaluation.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.047 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 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".