Bibliographic record
Abstract
George Pink and his colleagues have provided healthcare policy makers with a thorough review of pay-for-performance systems in healthcare. In general, their review suggests that pay-for-performance systems have resulted in few positive, net outcomes for health systems. Among other things, they cite the perverse incentives often generated by these systems, as well as these systems' high design and administration costs. The following article, building on research in economics, sociology and social psychology, extends their discussion by suggesting why healthcare delivery may be a uniquely difficult sector in which to rely on pay-for-performance systems. This article does not intend to shut down discussion of pay-for-performance in healthcare, but instead suggests how we might usefully think about when pay-for-performance is more or less appropriate. This analysis reveals that the healthcare delivery sector has some unique advantages over other sectors and industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.057 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".