ReviewNature2025
Intersectional analysis for science and technology.
Review in Nature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Childhood maltreatment and adult blood pressure-based outcomes: age- and sex-specific associations in the German National Cohort (NAKO).BMC public health · 2026Article
- Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.The Lancet. Digital health · 2026Article
- Socioeconomic differences in the prevalence, treatment and control of major cardiometabolic risk factors by sex: a cross-sectional study of the UK Biobank.BMJ public health · 2026Article
- New developments in women's health research and implications for nursing.Women's health nursing (Seoul, Korea) · 2025Article
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intersectionality describes interdependent systems of inequality related to sex, gender, race, age, class and other socio-political dimensions. By focusing on the compounded effects of social categories, intersectional analysis can enhance the accuracy and experimental efficiency of science. Here we extend intersectional approaches that were predominantly developed in the humanities, social sciences and public health to the fields of natural science and technology, where this type of analysis is less established. Informed by diverse global and disciplinary examples-from enhancing facial recognition for diverse user bases to mitigating the disproportionate impact of climate change on marginalized populations-we extract methods to demonstrate how quantitative intersectional analysis functions throughout the research process, from strategic considerations for establishing research priorities to formulating research questions, collecting and analysing data and interpreting results. Our goal is to offer a set of guidelines for researchers, peer-reviewed journals and funding agencies that facilitate systematic integration of intersectional analysis into relevant domains of science and technology. Precision in research best guides effective social and environmental policy aimed at achieving global equity and sustainability.
Indexed as
Identifiers
40205207What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.