The evidence gap on gendered impacts of performance-based financing among family physicians for chronic disease care: a systematic review reanalysis in contexts of single-payer universal coverage
Bibliographic record
Abstract
BACKGROUND: Although pay-for-performance (P4P) among primary care physicians for enhanced chronic disease management is increasingly common, the evidence base is fragmented in terms of socially equitable impacts in achieving the quadruple aim for healthcare improvement: better population health, reduced healthcare costs, and enhanced patient and provider experiences. This study aimed to assess the literature from a systematic review on how P4P for diabetes services impacts on gender equity in patient outcomes and the physician workforce. METHODS: A gender-based analysis was performed of studies retrieved through a systematic search of 10 abstract and citation databases plus grey literature sources for P4P impact assessments in multiple languages over the period January 2000 to April 2018, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The study was restricted to single-payer national health systems to minimize the risk of physicians sorting out of health organizations with a strong performance pay component. Two reviewers scored and synthesized the integration of sex and gender in assessing patient- and provider-oriented outcomes as well as the quality of the evidence. FINDINGS: Of the 2218 identified records, 39 studies covering eight P4P interventions in seven countries were included for analysis. Most (79%) of the studies reported having considered sex/gender in the design, but only 28% presented sex-disaggregated patient data in the results of the P4P assessment models, and none (0%) assessed the interaction of patients' sex with the policy intervention. Few (15%) of the studies controlled for the provider's sex, and none (0%) discussed impacts of P4P on the work life of providers from a gender perspective (e.g., pay equity). CONCLUSIONS: There is a dearth of evidence on gender-based outcomes of publicly funded incentivizing physician payment schemes for chronic disease care. As the popularity of P4P to achieve health system goals continues to grow, so does the risk of unintended consequences. There is a critical need for research integrating gender concerns to help inform performance-based health workforce financing policy options in the era of the Sustainable Development Goals.
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 | 0.044 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".