Cochrane reviews’ authorship has become more gender-diverse but remains geographically concentrated: a meta-research study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the distribution of country, region, language, and gender diversity in the authorship of Cochrane reviews and compare it to non-Cochrane systematic reviews. STUDY DESIGN AND SETTING: We retrieved all published articles from the Cochrane Library (until November 6, 2023) using a web crawling technique that extracted prespecified data fields, including publication date, review category, and author affiliations. For comparison, non-Cochrane systematic reviews were identified through PubMed using E-utility calls. We determined the country, region of affiliations and gender of the first, corresponding, and last authors for Cochrane reviews; the same fields were determined for first authors only for non-Cochrane reviews due to data availability. Trends in geographical and gender diversity over time were evaluated using logistic regression. Fisher's exact test was used for comparisons. Diversity trends between Cochrane and non-Cochrane reviews were explored through visual presentation, Pearson's product-moment correlation, and the Granger Causality Test. RESULTS: This comprehensive analysis included 22,681 Cochrane reviews and 224,484 non-Cochrane reviews. Cochrane reviews showed increasing diversity in several areas: representation of first authors from non-English speaking countries rose substantially (from 16.7% in 1996 to 42.8% in 2023), and female first authorship more than tripled (from 15.0% in 1996 to 55.6% in 2023). Representation from lower-and-middle-income countries (LMICs) in Cochrane reviews has declined recently (from a peak of 23.2% in 2012 to 18.4% in 2023). Among Cochrane Review Groups, diversity varied notably, with Sexually Transmitted Infections achieving the highest representation from LMICs (68.1% of first authors). In 2023, non-Cochrane reviews showed higher representation from non-English speaking countries (56.9%) and LMICs (50.8%) compared to Cochrane reviews. The patterns of gender diversity between Cochrane and non-Cochrane reviews showed strong correlations for female first authorship (r = 0.829, P < .001), suggesting parallel evolution over time. CONCLUSION: Both Cochrane and non-Cochrane reviews demonstrate important progress in author diversity, particularly in gender representation and inclusion of authors from non-English speaking countries. While non-Cochrane reviews show stronger representation from LMICs, both review sources reflect the evolving landscape of global evidence synthesis.
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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 | 0.057 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".