Setting priorities for knowledge translation of Cochrane reviews for health equity: Evidence for Equity
Bibliographic record
Abstract
BACKGROUND: A focus on equity in health can be seen in many global development goals and reports, research and international declarations. With the development of a relevant framework and methods, the Campbell and Cochrane Equity Methods Group has encouraged the application of an 'equity lens' to systematic reviews, and many organizations publish reviews intended to address health equity. The purpose of the Evidence for Equity (E4E) project was to conduct a priority-setting exercise and apply an equity lens by developing a knowledge translation product comprising summaries of systematic reviews from the Cochrane Library. E4E translates evidence from systematic reviews into 'friendly front end' summaries for policy makers. METHODS: The following topic areas with high burdens of disease globally, were selected for the pilot: diabetes/obesity, HIV/AIDS, malaria, nutrition, and mental health/depression. For each topic area, a "stakeholder panel" was assembled that included policymakers and researchers. A systematic search of Cochrane reviews was conducted for each area to identify equity-relevant interventions with a meaningful impact. Panel chairs developed a rating sheet which was used by all panels to rank the importance of these interventions by: 1) Ease of Implementation; 2) Health System Requirements; 3)Universality/Generalizability/Share of Burden; and 4) Impact on Inequities/Effect on equity. The ratings of panel members were averaged for each intervention and criterion, and interventions were ordered according to the average overall ratings. RESULTS: Stakeholder panels identified the top 10 interventions from their respective topic areas. The evidence on these interventions is being summarized with an equity focus and the results posted online, at http://methods.cochrane.org/equity/e4e-series . CONCLUSIONS: This method provides an explicit approach to setting priorities by systematic review groups and funders for providing decision makers with evidence for the most important equity-relevant interventions.
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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 | 0.673 | 0.847 |
| Meta-epidemiology (narrow) | 0.009 | 0.012 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.074 | 0.052 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.035 | 0.034 |
| Open science | 0.013 | 0.039 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".