Health equity: evidence synthesis and knowledge translation methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it