Systematic review protocol examining the effects of introducing pay-for-performance for primary care physicians on diabetes outcomes in single-payer healthcare systems (Preprint)
Bibliographic record
Abstract
BACKGROUND Although pay-for-performance (P4P) for diabetes care is increasingly common across health organizations, evidence of its effectiveness in improving population health and service delivery is deficient. This information gap is attributable in part to the heterogeneity of healthcare financing, covered medical conditions, care settings, and provider remuneration arrangements within and across countries. OBJECTIVE This paper outlines a protocol for a systematic review examining the effects of introducing P4P for physicians in primary care and community settings to support guideline-based diabetes care. Our aim is to reduce the heterogeneity of evidence presented that has deterred conclusiveness of previous reviews by narrowing the focus to disease-specific P4P schemes in single-payer healthcare insurance systems. This approach enables us to minimize the risk of unintended consequences of P4P such as physicians’ gaming the payment system. METHODS Our review systematically searches, appraises, and synthesizes the literature concentrating on whether P4P for primary care physicians leads to better diabetes outcomes in single-payer health systems. We search 10 electronic databases and manually scan the reference lists of review articles and other global health literature. We include primary studies evaluating the effects of introducing P4P for diabetes care among primary care physicians in countries of universal health coverage. Outcomes of interest include patient morbidity, avoidable hospitalization, premature death, and healthcare costs. RESULTS We have received funding from Diabetes Canada and the New Brunswick Health Research Foundation to conduct policy-actionable diabetes health services research. Database searches were conducted and full-texts screened by two reviewers in 2017. We aim to submit the review for publication in 2018. CONCLUSIONS We are narratively synthesizing the data. Because of the wide range of outcomes considered, we do not expect to perform a meta-analysis. Since database searches were completed prior to the publication of this protocol, it is ineligible for registration with PROSPERO.
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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 | 0.062 | 0.134 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.018 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.008 |
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".