Protocol for the development of a CONSORT-equity guideline to improve reporting of health equity in randomized trials
Bibliographic record
Abstract
BACKGROUND: Health equity concerns the absence of avoidable and unfair differences in health. Randomized controlled trials (RCTs) can provide evidence about the impact of an intervention on health equity for specific disadvantaged populations or in general populations; this is important for equity-focused decision-making. Previous work has identified a lack of adequate reporting guidelines for assessing health equity in RCTs. The objective of this study is to develop guidelines to improve the reporting of health equity considerations in RCTs, as an extension of the Consolidated Standards of Reporting Trials (CONSORT). METHODS/DESIGN: A six-phase study using integrated knowledge translation governed by a study executive and advisory board will assemble empirical evidence to inform the CONSORT-equity extension. To create the guideline, the following steps are proposed: (1) develop a conceptual framework for identifying "equity-relevant trials," (2) assess empirical evidence regarding reporting of equity-relevant trials, (3) consult with global methods and content experts on how to improve reporting of health equity in RCTs, (4) collect broad feedback and prioritize items needed to improve reporting of health equity in RCTs, (5) establish consensus on the CONSORT-equity extension: the guideline for equity-relevant trials, and (6) broadly disseminate and implement the CONSORT-equity extension. DISCUSSION: This work will be relevant to a broad range of RCTs addressing questions of effectiveness for strategies to improve practice and policy in the areas of social determinants of health, clinical care, health systems, public health, and international development, where health and/or access to health care is a primary outcome. The outcomes include a reporting guideline (CONSORT-equity extension) for equity-relevant RCTs and a knowledge translation strategy to broadly encourage its uptake and use by journal editors, authors, and funding agencies.
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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 | 0.283 | 0.478 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.103 | 0.052 |
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".