Where are the women? Gender inequalities in COVID-19 research authorship
Open accessAbstract
► Women account for about a third of all authors who published papers related to COVID-19 since the beginning of the outbreak in January 2020.Women's representation is lower still for first and last authorship positions.► Gender biases seem to be affecting COVID-19 research similar to other scientific areas, highlighting that women are consistently being under-represented.► This may have implications for the availability and interrogation of sex-disaggregated data and therefore our understanding of COVID-19.► These gender biases hint at wider gender inequalities in our global response to the pandemic, which may reduce the chance of dealing with it robustly and speedily.► Women are under-represented as authors of research papers in many scientific areas, particularly in senior authorship positions.
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During the pandemic, women in health care produced less academic work than men, evidenced by their lower representation among authors generating new pandemic knowledge.
“Burdened by these obligations, women had reduced academic productivity relative to men, as evidenced by fewer women being part of the cohort producing new knowledge about the pandemic.10”
From COVID-19: a heavy toll on health-care workers (2021) · cites this paper as [10]
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Mitigating the wider health effects of covid-19 pandemic response
Douglas, M., Katikireddi, S. V., Taulbut, M., McKee, M., & McCartney, G. (2020) · BMJ · doi:10.1136/bmj.m1557 · Cited by 1,105 · see citing works
Filardo, G., da Graca, B., Sass, D. M., Pollock, B. D., Smith, E. B., & Martinez, M. A.-M. (2016) · BMJ · doi:10.1136/bmj.i847 · Cited by 456 · see citing works
The pandemic and the female academic
Minello, A. (2020) · Nature · doi:10.1038/d41586-020-01135-9 · Cited by 277 · see citing works
Nielsen, M. W., Andersen, J. P., Schiebinger, L. L., & Schneider, J. W. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0235-x · Cited by 233 · see citing works
Governance of the Covid-19 response: a call for more inclusive and transparent decision-making
Rajan, D., Koch, K., Rohrer-Herold, K., Bajnoczki, C., Socha, A., Voss, M., Nicod, M., Ridde, V., & Koonin, J. (2020) · BMJ Global Health · doi:10.1136/bmjgh-2020-002655 · Cited by 191 · see citing works
Lerchenmüller, C., Lerchenmueller, M., & Sorenson, O. (2018) · Circulation · doi:10.1161/circulationaha.117.032325 · Cited by 75 · see citing works
The gender gap in first authorship of research papers
Rexrode, K. M. (2016) · BMJ · doi:10.1136/bmj.i1130 · Cited by 35 · see citing works
(2012) · Nature · doi:10.1038/491495a · Cited by 32 · see citing works
Gender equality in academic research on epilepsy—a study on scientific authorships
Bendels, M. H. K., Wanke, E. M., Schöffel, N., et al. (2017) · Epilepsia · doi:10.1111/epi.13873 · Cited by 29 · see citing works
Nature’s under-representation of women
(2018) · Nature · doi:10.1038/d41586-018-05465-7 · Cited by 28 · see citing works
(2012) · PubMed · Cited by 13 · see citing works
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Research works printed but not linked: 1
- [8]Holmes A, Hardy S Gender bias in peer review – opening up the black box. London, UK: London School of Economics, 2019.
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COVID-19: a heavy toll on health-care workers
Mehta, S., Machado, F., Kwizera, A., Papazian, L., Moss, M., Azoulay, É., & Herridge, M. (2021) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(21)00068-0 · Cited by 381 · see citing works
Ruiz-Frutos, C., Ortega-Moreno, M., Allande-Cussó, R., Domínguez-Salas, S., Dias, A., & Gómez-Salgado, J. (2021) · Safety Science · doi:10.1016/j.ssci.2020.104996 · Cited by 76 · see citing works
- Authors
- Pinho-Gomes, A.-C., Peters, S., Thompson, K., Hockham, C., Ripullone, K., Woodward, M., & Carcel, C.
- Affiliations
- The George Institute for Global Health, University of Oxford, Oxford, UK
- The George Institute for Global Health (GB)
- UNSW Sydney (AU)
- Johns Hopkins University (US)
- ORCID
- Venue
- BMJ Global Health
- Publisher
- BMJ
- Published
- 2020-06-11
- Volume
- 5
- Issue
- 7
- Pages
- e002922
- ISSN
- 2059-7908 (ISSN-L)
- PMC
- PMC7298677
- OpenAlex
- W3034617800
- Cited by
- 270
- References
- 11
- Citations by year
- 2026: 2 · 2025: 49 · 2024: 20 · 2023: 36 · 2022: 57 · 2021: 76 · 2020: 30
- FWCI
- 71.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Global Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of New South Wales
- Topics
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- gender inequality, COVID-19 research, authorship, women in science, sex-disaggregated data, gender bias in science, pandemic response
- MeSH terms
- COVID-19, Authorship, Female, Humans, Male, Periodicals as Topic, Pneumonia, Viral, Bibliometrics, Sex Distribution, Coronavirus Infections, Biomedical Research, Pandemics, Sexism
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