Is the evidence on the effectiveness of pay for performance schemes in healthcare changing? Evidence from a meta-regression analysis
Bibliographic record
Abstract
BACKGROUND: This study investigated if the evidence on the success of the Pay for Performance (P4P) schemes in healthcare is changing as the schemes continue to evolve by updating a previous systematic review. METHODS: A meta-regression analysis using 116 studies evaluating P4P schemes published between January 2010 to February 2018. The effects of the research design, incentive schemes, use of incentives, and the size of the payment to revenue ratio on the proportion of statically significant effects in each study were examined. RESULTS: There was evidence of an increase in the range of countries adopting P4P schemes and weak evidence that the proportion of studies with statistically significant effects have increased. Factors hypothesized to influence the success of schemes have not changed. Studies evaluating P4P schemes which made payments for improvement over time, were associated with a lower proportion of statistically significant effects. There was weak evidence of a positive association between the incentives' size and the proportion of statistically significant effects. CONCLUSION: The evidence on the effectiveness of P4P schemes is evolving slowly, with little evidence that lessons are being learned concerning the design and evaluation of P4P schemes.
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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 | 0.136 | 0.326 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.060 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".