Equity in evidence synthesis: you can’t play on broken strings
Bibliographic record
Abstract
The 2022 Cochrane Lecture challenged Cochrane and its community to enhance their equity efforts and presented specific issues needing attention. We have created an actionable plan to address these challenges. These include: 1. Setting priorities for reviews based on health equity and global burden of disease 2. Advocate and support initiatives to promote equity, diversity, and inclusion in Cochrane 3. Actively seek global representation in the governance of the Campbell and Cochrane Health Equity Thematic Group We advocate for the inclusion of health equity considerations in all Cochrane systematic reviews. To achieve this, we need the cooperation of all those involved, such as journal editors, funders, and researchers. We invite the global community to work with us to address these equity gaps in research and practice.
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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 | 0.487 | 0.853 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.027 | 0.046 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.022 | 0.038 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".