Extending the PRISMA statement to equity-focused systematic reviews (PRISMA-E 2012): explanation and elaboration
Bibliographic record
Abstract
The promotion of health equity, the absence of avoidable and unfair differences in health outcomes, is a global imperative. Systematic reviews are an important source of evidence for health decision-makers, but have been found to lack assessments of the intervention effects on health equity. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) is a 27-item checklist intended to improve the transparency and reporting of systematic reviews. We developed an equity extension for PRISMA (PRISMA-E 2012) to help systematic reviewers identify, extract, and synthesise evidence on equity in systematic reviews. In this explanation and elaboration paper we provide the rationale for each extension item. These items are additions or modifications to the existing PRISMA Statement items, in order to incorporate a focus on equity. An example of good reporting is provided for each item as well as the original PRISMA item. This explanation and elaboration document is intended to accompany the PRISMA-E 2012 Statement and the PRISMA Statement to improve understanding of the reporting guideline for users. The PRISMA-E 2012 reporting guideline is intended to improve the transparency and completeness of the reporting of equity-focused systematic reviews. Improved reporting can lead to better judgement of applicability by policymakers, which may result in more appropriate policies and programmes and may contribute to reductions in health inequities. To encourage wide dissemination of this article it is accessible on the International Journal for Equity in Health, Journal of Clinical Epidemiology, and Journal of Development Effectiveness websites.
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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 | 0.498 | 0.682 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".