PRO EDI -- a tool to help systematic reviewers make equity, diversity and inclusion assessments
Bibliographic record
Abstract
Introduction: Decisions need evidence, and for healthcare decisions, the evidence decision-makers often want is a systematic review. However, reviews often lack clarity about who is represented within the evidence they synthesize, which limits understanding of how findings apply to diverse populations. PRO EDI was developed to help systematic review authors extract and report equity-related participant data to support greater transparency and more informed judgments about applicability. Methods: PRO EDI was developed iteratively between August 2022 and March 2024 and was conceptualized as a way of making it easier to use PROGRESS-Plus, a framework to assess equity in reviews. An initial draft was created and then discussed and revised in collaboration with an international advisory group. A relatively mature version of the tool was then presented to a meeting of the Cochrane Health Equity Thematic Group. The modified version that emerged from that meeting was considered v1 of PRO EDI. Results: PRO EDI has two main components: a participant characteristics table and guidance on how to use the extracted characteristics data within reviews. PRO EDI recommends that six participant characteristics should be extracted for all included studies in a review: age, sex, gender, ethnicity, race and ancestry, socioeconomic status, and location. Other characteristics (e.g., disability) may be important for some reviews. PRO EDI is relevant for all systematic reviews, not just those with an equity focus. The tool has been piloted in several reviews and is publicly available via Trial Forge. Conclusion: PRO EDI gives systematic review authors a consistent way of deciding which participant characteristics to extract from included studies to support equity-related judgments in their results and discussion. It also suggests ways in which those judgments can be presented.
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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 | 0.396 | 0.680 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.043 | 0.032 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.009 | 0.024 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.115 | 0.029 |
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".