Topics of Women’s Health Research in the United States, Canada, European Union, and Japan in 2012-2022
Bibliographic record
Abstract
Purpose: This study investigated recent trends in the topics of women's health research in the United States (US), Canada, Japan, and the European Union where various research projects related to women and gender have been conducted.Methods: To explore recent international women's health research, we selected research projects conducted between 2012 and 2022 from the US National Institute of Health, Canada's Research Information System, Japan's Health, Labor and Welfare Science Research Performance Database, and the European Union's CORDIS website. We categorized the identified research into three main areas; common or serious diseases and conditions affecting women, diseases, and conditions specific to women, and factors influencing women's health.Results: The focus of health research expanded beyond traditional views of women, gender, and gender differences. Projects addressed the health needs of vulnerable groups, including refugees, migrants, incarcerated women, trans individuals, and pregnant women with autism. They also explored the connections between gender and racial differences in women's health. This inclusive approach considered the gaps and intersections within women's health.Conclusion: Future women's health research in Korea will need to consider the intersectionality of gender, aging, and immigration. Environment-based approach in the research of drug addiction, mental health, nursing, and care would be important.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".