Incorporating an equity perspective in systematic reviews of interventions: potential methodological approaches
Bibliographic record
Abstract
Health inequities are unnecessary, avoidable and unjust differences in health across social groups. Addressing them is a priority for governments and health systems worldwide, requiring not only specific interventions targeting inequity but also embedding equity across all decision-making. Systematic reviews of interventions underpin health decision-making and could, therefore, be a key mechanism to address inequities, but most reviews are limited in their approach to considering equity and often only conclude data for subgroup analyses are unavailable. While some guidance is available, it largely focuses on reviews of interventions specifically seeking to reduce inequities and is published in disparate literature. We describe approaches to incorporate an equity perspective relevant to all systematic reviews of interventions, even when equity is not the primary review focus.Consideration of equity may be needed at all stages of the review process. Planning the review involves examining theory, using logic models, involving relevant people and organisations, and considering if additional sources of evidence are needed. Investigating the data requires examining the external validity of primary studies, including who was involved in the primary studies, and the reach of interventions. The synthesis process includes selecting appropriate analysis, considering the implications of reporting absolute or relative equity effects of the intervention, exploring and understanding mechanisms and assessing certainty of the evidence in relation to equity. Interpreting results involves linking theory with evidence and discussing implications and limitations. We hope this article helps review authors make best use of the available evidence to incorporate equity into systematic reviews.
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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 | 0.698 | 0.842 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.036 | 0.031 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".