Improving social justice in observational studies: protocol for the development of a global and Indigenous STROBE-equity reporting guideline
Bibliographic record
Abstract
BACKGROUND: Addressing persistent and pervasive health inequities is a global moral imperative, which has been highlighted and magnified by the societal and health impacts of the COVID-19 pandemic. Observational studies can aid our understanding of the impact of health and structural oppression based on the intersection of gender, race, ethnicity, age and other factors, as they frequently collect this data. However, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline, does not provide guidance related to reporting of health equity. The goal of this project is to develop a STROBE-Equity reporting guideline extension. METHODS: We assembled a diverse team across multiple domains, including gender, age, ethnicity, Indigenous background, disciplines, geographies, lived experience of health inequity and decision-making organizations. Using an inclusive, integrated knowledge translation approach, we will implement a five-phase plan which will include: (1) assessing the reporting of health equity in published observational studies, (2) seeking wide international feedback on items to improve reporting of health equity, (3) establishing consensus amongst knowledge users and researchers, (4) evaluating in partnership with Indigenous contributors the relevance to Indigenous peoples who have globally experienced the oppressive legacy of colonization, and (5) widely disseminating and seeking endorsement from relevant knowledge users. We will seek input from external collaborators using social media, mailing lists and other communication channels. DISCUSSION: Achieving global imperatives such as the Sustainable Development Goals (e.g., SDG 10 Reduced inequalities, SDG 3 Good health and wellbeing) requires advancing health equity in research. The implementation of the STROBE-Equity guidelines will enable a better awareness and understanding of health inequities through better reporting. We will broadly disseminate the reporting guideline with tools to enable adoption and use by journal editors, authors, and funding agencies, using diverse strategies tailored to specific audiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".