Development and Evaluation of ‘Briefing Notes’ as a Novel Knowledge Translation Tool to Aid the Implementation of Sex/Gender Analysis in Systematic Reviews: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of sex/gender differences in health and the importance of identifying differential effects of interventions for men and women. Yet, to whom the research evidence does or does not apply, with regard to sex/gender, is often insufficiently answered. This is also true for systematic reviews which synthesize results of primary studies. A lack of analysis and reporting of evidence on sex/gender raises concerns about the applicability of systematic reviews. To bridge this gap, this pilot study aimed to translate knowledge about sex/gender analysis (SGA) into a user-friendly 'briefing note' format and evaluate its potential in aiding the implementation of SGA in systematic reviews. METHODS: Our Sex/Gender Methods Group used an interactive process to translate knowledge about sex/gender into briefing notes, a concise communication tool used by policy and decision makers. The briefing notes were developed in collaboration with three Cochrane Collaboration review groups (HIV/AIDS, Hypertension, and Musculoskeletal) who were also the target knowledge users of the briefing notes. Briefing note development was informed by existing systematic review checklists, literature on sex/gender, in-person and virtual meetings, and consultation with topic experts. Finally, we held a workshop for potential users to evaluate the notes. RESULTS: Each briefing note provides tailored guidance on considering sex/gender to reviewers who are planning or conducting systematic reviews and includes the rationale for considering sex/gender, with examples specific to each review group's focus. Review authors found that the briefing notes provided welcome guidance on implementing SGA that was clear and concise, but also identified conceptual and implementation challenges. CONCLUSIONS: Sex/gender briefing notes are a promising knowledge translation tool. By encouraging sex/gender analysis and equity considerations in systematic reviews, the briefing notes can assist systematic reviewers in ensuring the applicability of research evidence, with the goal of improved health outcomes for diverse populations.
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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 | 0.373 | 0.562 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".