Improving the Reporting on Health Equity in Observational Research (STROBE-Equity)
Bibliographic record
Abstract
Importance: Observational studies can provide valuable insights to inform decisions on health equity. Existing guidelines for reporting such studies, such as the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement, currently lack specific considerations for reporting on health equity. Health equity is defined as the absence of avoidable and unfair differences that may exist across individuals and populations due to structural and systematic inequities in living and working conditions, opportunities, and resources. To address this gap, the research team developed an extension of the STROBE statement (STROBE-Equity) that focuses on reporting health equity data and considerations. Observations: This consensus statement followed steps for developing a consensus- and evidence-based guideline using an integrated knowledge translation approach to ensure engagement of knowledge users from diverse disciplines and perspectives. Selection criteria for the research team and steering committees prioritized diversity across age, gender, and geography. The STROBE checklist was extended to include 10 items specifically aimed at reporting health equity considerations. To develop these items, the research team drew on evidence from empirical studies including a scoping review of the literature, methodological review, key informant interviews, an online survey, and a global consensus meeting of experts. For each of the 10 equity-related items, the statement provides an explanation and example(s) of transparent reporting practices. Conclusions and Relevance: Use of the STROBE-Equity extension alongside the STROBE statement when writing up completed reports of observational studies has the potential to advance the reporting of health equity data and considerations. Improved reporting of this information may help knowledge users better identify and apply evidence relevant to populations experiencing inequities.
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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 | 0.804 | 0.941 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".