Sex/gender reporting and analysis in Campbell and Cochrane systematic reviews: a cross-sectional methods study
Bibliographic record
Abstract
BACKGROUND: The importance of sex and gender considerations in research is being increasingly recognized. Evidence indicates that sex and gender can influence intervention effectiveness. We assessed the extent to which sex/gender is reported and analyzed in Campbell and Cochrane systematic reviews. METHODS: We screened all the systematic reviews in the Campbell Library (n = 137) and a sample of systematic reviews from 2016 to 2017 in the Cochrane Library (n = 674). We documented the frequency of sex/gender terms used in each section of the reviews. RESULTS: We excluded 5 Cochrane reviews because they were withdrawn or published and updated within the same time period as well as 4 Campbell reviews and 114 Cochrane reviews which only included studies focused on a single sex. Our analysis includes 133 Campbell reviews and 555 Cochrane reviews. We assessed reporting of sex/gender considerations for each section of the systematic review (Abstract, Background, Methods, Results, Discussion). In the methods section, 83% of Cochrane reviews (95% CI 80-86%) and 51% of Campbell reviews (95% CI 42-59%) reported on sex/gender. In the results section, less than 30% of reviews reported on sex/gender. Of these, 37% (95% CI 29-45%) of Campbell and 75% (95% CI 68-82%) of Cochrane reviews provided a descriptive report of sex/gender and 63% (95% CI 55-71%) of Campbell reviews and 25% (95% CI 18-32%) of Cochrane reviews reported analytic approaches for exploring sex/gender, such as subgroup analyses, exploring heterogeneity, or presenting disaggregated data by sex/gender. CONCLUSION: Our study indicates that sex/gender reporting in Campbell and Cochrane reviews is inadequate.
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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 | 0.412 | 0.730 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.035 | 0.045 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".