Research priorities and considerations for nutrition research: methods of sex and gender analysis for biomedical and nutrition research
Bibliographic record
Abstract
For some 20 years, science funding bodies have been asking for the integration of sex- and gender-related factors into the content of research and innovation. The rationale for those requirements has been the accumulated evidence that sex and gender are important determinants of health and disease. The European Commission (EC) has been the first, since 2002, to seriously ask for the integration of sex and gender into research and innovation in the context of their multi-annual framework programmes. When introduced, this condition was not immediately applauded by the research community, who perhaps lacked training in methods for the integration of sex- and gender-related factors. The EC Expert Group on Gendered Innovations sought to fill this gap. This review describes the work of this international collaborative project which has resulted in the development of general and field-specific methods for sex and gender analysis and 38 case studies for various research domains (science, health and medicine, environment, engineering) to illustrate how, by applying methods of sex and gender analysis, new knowledge could be created. Since 2010, science funding bodies in Canada, the USA and several EU member states have followed the example of the EC issuing similar conditions. Although the effects of nutritional patterns on a range of (physiological and health) outcomes may differ for men and women, sex and gender analyses are rarely conducted in nutrition research. In this review, we provide examples of how gender is connected to dietary intake, and how advancing gender analysis may inform gender-sensitive policies and dietary recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".