When is a randomised controlled trial health equity relevant? Development and validation of a conceptual framework
Bibliographic record
Abstract
BACKGROUND: Randomised controlled trials can provide evidence relevant to assessing the equity impact of an intervention, but such information is often poorly reported. We describe a conceptual framework to identify health equity-relevant randomised trials with the aim of improving the design and reporting of such trials. METHODS: An interdisciplinary and international research team engaged in an iterative consensus building process to develop and refine the conceptual framework via face-to-face meetings, teleconferences and email correspondence, including findings from a validation exercise whereby two independent reviewers used the emerging framework to classify a sample of randomised trials. RESULTS: A randomised trial can usefully be classified as 'health equity relevant' if it assesses the effects of an intervention on the health or its determinants of either individuals or a population who experience ill health due to disadvantage defined across one or more social determinants of health. Health equity-relevant randomised trials can either exclusively focus on a single population or collect data potentially useful for assessing differential effects of the intervention across multiple populations experiencing different levels or types of social disadvantage. Trials that are not classified as 'health equity relevant' may nevertheless provide information that is indirectly relevant to assessing equity impact, including information about individual level variation unrelated to social disadvantage and potentially useful in secondary modelling studies. CONCLUSION: The conceptual framework may be used to design and report randomised trials. The framework could also be used for other study designs to contribute to the evidence base for improved health equity.
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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 | 0.822 | 0.867 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.025 | 0.011 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.014 | 0.015 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".