ReviewThe Journal of neuroscience : the official journal of the Society for Neuroscience2019
Gender in Science, Technology, Engineering, and Mathematics: Issues, Causes, Solutions.
Review in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Factors Associated With STEAM Career Interest Among Female Students in Bangladesh: A Cross-Sectional Study.Health science reports · 2026Article
- Changing Patterns of Gender Representation in Canada's Technology Sector and the Care Economy: Two Differing Tales.Canadian review of sociology = Revue canadienne de sociologie · 2026Article
- Children's math and science beliefs about underrepresented peers are related to STEM occupation expectations.Journal of experimental child psychology · 2026Article
- Mentorship, memory, and honor: Rita Levi-Montalcini's legacy and the role of academic recognition in promoting women in science.Frontiers in molecular neuroscience · 2026Review
- Children and Young Adults Factor Merit Into Their Judgments of Gender-Based Science Resource Inequalities.Developmental science · 2026Article
- Article
- Stereotypical bias amplification and reversal in an experimental model of human interaction with generative artificial intelligence.Royal Society open science · 2025Article
- Exploring the Italian Population's attitudes toward health data sharing for healthcare purpose and scientific research: a cross-sectional study.Journal of public health (Oxford, England) · 2025Article
- The Influence of Demographic Characteristics on Student Academic Performance in University Admission Tests.Psychology in Russia : state of the art · 2025Article
- Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis.Journal of medical Internet research · 2024Article
- Seven Strategies to Integrate Equity within Translational Research in Neurology.Annals of neurology · 2024Review
- Turnover behavior and intention among dentists and medical doctors: a cross-sectional study in China.BMC oral health · 2024Article
- Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.Frontiers in public health · 2024Article
- Comparative analysis of gender disparity in academic positions based on U.S. region and STEM discipline.PloS one · 2024Article
- Editorial: Women in oral health promotion.Frontiers in oral health · 2024Article
- Editorial: Women in ethnopharmacology: 2023.Frontiers in pharmacology · 2024Article
- Gender equity in the scientific nursing journals indexed in Journal Citation Reports: A cross-sectional study.Frontiers in public health · 2023Article
- The cost of social influence: Own-gender and gender-stereotype social learning biases in adolescents and adults.PloS one · 2023Article
- Not yet defect-free: the current landscape for women in computational materials research.npj computational materials · 2023Article
- Editorial: Women in aquatic microbiology: 2022.Frontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The landscape of gender in education and the workforce has shifted over the past decades: women have made gains in representation, equitable pay, and recognition through awards, grants, and publications. Despite overall change, differences persist in the fields of science, technology, engineering, and mathematics (STEM). This Viewpoints article on gender disparities in STEM offers an overarching perspective by addressing what the issues are, why the issues may emerge, and how the issues may be solved. In Part 1, recent data on gaps in representation, compensation, and recognition (awards, grants, publications) are reviewed, highlighting differences across subfields (e.g., computer science vs biology) and across career trajectories (e.g., bachelor's degrees vs senior faculty). In Part 2, evidence on leading explanations for these gaps, including explanations centered on abilities, preferences, and explicit and implicit bias, is presented. Particular attention is paid to implicit bias: mental processes that exist largely outside of conscious awareness and control in both male and female perceivers and female targets themselves. Given its prevalence and persistence, implicit bias warrants a central focus for research and application. Finally, in Part 3, the current knowledge is presented on interventions to change individuals' beliefs and behaviors, as well as organizational culture and practices. The moral issues surrounding equal access aside, understanding and addressing the complex issues surrounding gender in STEM are important because of the possible benefits to STEM and society that will be realized only when full participation of all capable and qualified individuals is guaranteed.
Indexed as
Identifiers
What 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.