Reporting of health equity considerations in equity-relevant observational studies: Protocol for a systematic assessment
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> The mitigation of unfair and avoidable differences in health is an increasing global priority. Observational studies including cohort, cross-sectional and case-control studies tend to report social determinants of health which could inform evidence syntheses on health equity and social justice. However, the extent of reporting and analysis of equity in equity-relevant observational studies is unknown. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> We define studies which report outcomes for populations at risk of experiencing inequities as “equity-relevant”. Using a random sampling technique we will identify 320 equity-relevant observational studies published between 1 January 2020 to 27 April 2022 by searching the MEDLINE database. We will stratify sampling by 1) studies in high-income countries (HIC) and low- and middle-income countries (LMIC) according to the World Bank classification, 2) studies focused on COVID and those which are not, 3) studies focused on populations at risk of experiencing inequities and those on general populations that stratify their analyses. We will use the PROGRESS framework which stands for place of residence, race or ethnicity, occupation, gender or sex, religion, education, socioeconomic status, social capital, to identify dimensions where inequities may exist. Using a previously developed data extraction form we will pilot-test on eligible studies and revise as applicable. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> The proposed methodological assessment of reporting will allow us to systematically understand the current reporting and analysis practices for health equity in observational studies. The findings of this study will help inform the development of the equity extension for the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) reporting guidelines. </ns3:p>
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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 | 0.069 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".