Using PROGRESS-plus to identify current approaches to the collection and reporting of equity-relevant data: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to identify what and how data relating to the social determinants of health are collected and reported in equity-relevant studies and map these data to the PROGRESS-Plus framework. STUDY DESIGN AND SETTING: We performed a scoping review. We ran two systematic searches of MEDLINE and Embase for equity-relevant studies published during 2021. We included studies in any language without limitations to participant characteristics. Included studies were required to have collected and reported at least two participant variables relevant to evaluating individual-level social determinants of health. We applied the PROGRESS-Plus framework to identify and organize these data. RESULTS: We extracted data from 200 equity-relevant studies, providing 962 items defined by PROGRESS-Plus. A median of 4 (interquartile range = 2) PROGRESS-Plus items were reported in the included studies. 92% of studies reported age; 78% reported sex/gender; 65% reported educational attainment; 49% reported socioeconomic status; 45% reported race; 44% reported social capital; 33% reported occupation; 14% reported place and 9% reported religion. CONCLUSION: Our synthesis demonstrated that researchers currently collect a limited range of equity-relevant data, but usefully provides a range of examples spanning PROGRESS-Plus to inform the development of improved, standardized practices.
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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 | 0.331 | 0.566 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.103 | 0.071 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".