Does Consideration and Assessment of Effects on Health Equity Affect the Conclusions of Systematic Reviews? A Methodology Study
Bibliographic record
Abstract
INTRODUCTION: Tackling health inequities both within and between countries remains high on the agenda of international organizations including the World Health Organization and local, regional and national governments. Systematic reviews can be a useful tool to assess effects on equity in health status because they include studies conducted in a variety of settings and populations. This study aims to describe the extent to which the impacts of health interventions on equity in health status are considered in systematic reviews, describe methods used, and assess the implications of their equity related findings for policy, practice and research. METHODS: We conducted a methodology study of equity assessment in systematic reviews. Two independent reviewers extracted information on the reporting and analysis of impacts of health interventions on equity in health status in a group of 300 systematic reviews collected from all systematic reviews indexed in one month of MEDLINE, using a pre-tested data collection form. Any differences in data extraction were resolved by discussion. RESULTS: Of the 300 systematic reviews, 224 assessed the effectiveness of interventions on health outcomes. Of these 224 reviews, 29 systematic reviews assessed effects on equity in health status using subgroup analysis or targeted analyses of vulnerable populations. Of these, seven conducted subgroup analyses related to health equity which were reported in insufficient detail to judge their credibility. Of these 29 reviews, 18 described implications for policy and practice based on assessment of effects on health equity. CONCLUSION: The quality and completeness of reporting should be enhanced as a priority, because without this policymakers and practitioners will continue lack the evidence base they need to inform decision-making about health inequity. Furthermore, there is a need to develop methods to systematically consider impacts on equity in health status that is currently lacking in 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 | 0.923 | 0.963 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.027 | 0.032 |
| Bibliometrics | 0.041 | 0.044 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.031 | 0.049 |
| Open science | 0.014 | 0.025 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".