The challenges of including sex/gender analysis in systematic reviews: a qualitative survey
Bibliographic record
Abstract
BACKGROUND: Systematic review methodology includes the rigorous collection, selection, and evaluation of data in order to synthesize the best available evidence for health practice, health technology assessments, and health policy. Despite evidence that sex and gender matter to health outcomes, data and analysis related to sex and gender are frequently absent in systematic reviews, raising concerns about the quality and applicability of reviews. Few studies have focused on challenges to implementing sex/gender analysis within systematic reviews. METHODS: A multidisciplinary group of systematic reviewers, methodologists, biomedical and social science researchers, health practitioners, and other health sector professionals completed an open-ended survey prior to a two-day workshop focused on sex/gender, equity, and bias in systematic reviews. Respondents were asked to identify challenging or 'thorny' issues associated with integrating sex and gender in systematic reviews and indicate how they address these in their work. Data were analysed using interpretive description. A summary of the findings was presented and discussed with workshop participants. RESULTS: Respondents identified conceptual challenges, such as defining sex and gender, methodological challenges in measuring and analysing sex and gender, challenges related to availability of data and data quality, and practical and policy challenges. No respondents discussed how they addressed these challenges, but all proposed ways to address sex/gender analysis in the future. CONCLUSIONS: Respondents identified a wide range of interrelated challenges to implementing sex/gender considerations within systematic reviews. To our knowledge, this paper is the first to identify these challenges from the perspectives of those conducting and using systematic reviews. A framework and methods to integrate sex/gender analysis in systematic reviews are in the early stages of development. A number of priority items and collaborative initiatives to guide systematic reviewers in sex/gender analysis are provided, based on the survey results and subsequent workshop discussions. An emerging 'community of practice' is committed to enhancing the quality and applicability of systematic reviews by integrating considerations of sex/gender into the review process, with the goals of improving health outcomes and ensuring health equity for all persons.
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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 | 0.342 | 0.469 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".