Training in the implementation of sex and gender research policies: an evaluation of publicly available online courses
Bibliographic record
Abstract
BACKGROUND: Recently implemented research policies requiring the inclusion of females and males have created an urgent need for effective training in how to account for sex, and in some cases gender, in biomedical studies. METHODS: Here, we evaluated three sets of publicly available online training materials on this topic: (1) Integrating Sex & Gender in Health Research from the Canadian Institutes of Health Research (CIHR); (2) Sex as a Biological Variable: A Primer from the United States National Institutes of Health (NIH); and (3) The Sex and Gender Dimension in Biomedical Research, developed as part of "Leading Innovative measures to reach gender Balance in Research Activities" (LIBRA) from the European Commission. We reviewed each course with respect to their coverage of (1) What is required by the policy; (2) Rationale for the policy; (3) Handling of the concepts "sex" and "gender;" (4) Research design and analysis; and (5) Interpreting and reporting data. RESULTS: All three courses discussed the importance of including males and females to better generalize results, discover potential sex differences, and tailor treatments to men and women. The entangled nature of sex and gender, operationalization of sex, and potential downsides of focusing on sex more than other sources of variation were minimally discussed. Notably, all three courses explicitly endorsed invalid analytical approaches that produce bias toward false positive discoveries of difference. CONCLUSIONS: Our analysis suggests a need for revised or new training materials that incorporate four major topics: precise operationalization of sex, potential risks of over-emphasis on sex as a category, recognition of gender and sex as complex and entangled, and rigorous study design and data analysis.
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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 | 0.171 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".