Exploring perspectives of knowledge users about reporting on health equity in observational studies: A qualitative study informing the development of the STROBE-Equity reporting guideline
Bibliographic record
Abstract
BACKGROUND: Health inequities arising from systemic factors and contextual conditions result in avoidable and unjust differences in health outcomes, with profound social and economic implications. Health inequities can frequently go unreported in observational studies. Observational studies can uniquely inform how we understand and address persisting health inequities through collecting, reporting and analyzing health equity factors using 'inclusive' methodological considerations. While the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) reporting guideline aims to improve observational study reporting quality, existing extensions lacked a specific focus on health equity. Engaging those who will use or be impacted by research ("knowledge users"), including participant-research collaborators, is central to bridging the gap between knowledge production and real-world application. Therefore, the purpose of this study was to gather a range of perspectives from these knowledge users as part of, and to inform, the development of the STROBE-Equity guideline extension, which aims to improve the reporting of equity-relevant considerations in observational studies. METHODS: This study used a qualitative description approach, employing semi-structured key informant interviews and framework analysis to collect and analyze the views of researchers, policymakers, decision-makers, funders, journal editors, ethicists, and participant-research collaborators. Participants were purposefully sampled to reflect diverse perspectives from knowledge users with relevant experience on health equity reporting. RESULTS: Eleven key informants participated in the interviews. Information from interviews was categorized into seven themes: "Clarifying equity", "Equity is dynamic", "The challenges of making equity claims", "Making reporting on equity feasible", "Using reporting guidelines to manage tension", "Potential for better outcomes", and "Nobody's perspective is neutral". Participants emphasized the need for standardized equity-relevant reporting practices and offered insight into challenges, opportunities, and strategies for integrating equity-relevant considerations into observational study reporting. CONCLUSIONS: Findings show that participants viewed equity as a complex concept and stressed the need for practical guidance to support equity reporting in observational studies. They highlighted barriers such as limited time, resources, and publication word limits, and perceived the STROBE-Equity extension as a valuable tool for structuring reporting, raising awareness, and encouraging reflection, with the potential to improve the quality and impact of equity-relevant research.
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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 | 0.253 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".