Health equity: evidence synthesis and knowledge translation methods
Bibliographic record
Abstract
BACKGROUND: At the Rio Summit in 2011 on Social Determinants of Health, the global community recognized a pressing need to take action on reducing health inequities. This requires an improved evidence base on the effects of national and international policies on health inequities. Although systematic reviews are recognized as an important source for evidence-informed policy, they have been criticized for failing to assess effects on health equity. METHODS: This article summarizes guidance on both conducting systematic reviews with a focus on health equity and on methods to translate their findings to different audiences. This guidance was developed based on a series of methodology meetings, previous guidance, a recently developed reporting guideline for equity-focused systematic reviews (PRISMA-Equity 2012) and a systematic review of methods to assess health equity in systematic reviews. RESULTS: We make ten recommendations for conducting equity-focused systematic reviews; and five considerations for knowledge translation. Illustrative examples of equity-focused reviews are provided where these methods have been used. CONCLUSIONS: Implementation of the recommendations in this article is one step toward monitoring the impact of national and international policies and programs on health equity, as recommended by the 2011 World Conference on Social Determinants of Health.
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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 | 0.403 | 0.635 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".