Diversity in science is necessary to improve innovation and increase the capacity of the scientific workforce. Despite decades-long efforts to increase gender diversity, however, women remain a small minority in many fields, especially in senior positions. The dearth of elite women scientists, in turn, leaves fewer women to serve as mentors and role models for young women scientists. To shed light on gender disparities in science, we study prominent scholars who were elected to the National Academy of Sciences. We construct author citation networks that capture the structure of recognition among scholars' peers. We identify gender disparities in the patterns of peer citations and show that these differences are strong enough to accurately predict the scholar's gender. In contrast, we do not observe disparities due to prestige, with few significant differences in the structure of citations of scholars affiliated with high-ranked and low-ranked institutions. These results provide further evidence that a scholar's gender plays a role in the mechanisms of success in science.
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http://dx.doi.org/10.1073/pnas.2206070119 | DOI Listing |
BMC Pregnancy Childbirth
January 2025
Centre for Maternal and Child Health Research, City St George's, University of London, Myddelton Street Building, 1 Myddelton Street, London, EC1R 1UB, United Kingdom.
Background: In the United Kingdom, induction of labour rates are rapidly rising, and around a third of pregnant women undergo the procedure. The first stage, cervical ripening, traditionally carried out in hospital, is increasingly offered outpatient - or 'at home'. The current induction of labour rates place considerable demand on maternity services and impact women's experiences of care, and at home cervical ripening has been suggested as potential solution for alleviating these.
View Article and Find Full Text PDFWomens Health Rep (New Rochelle)
January 2025
National Initiative on Gender, Culture and Leadership in Medicine: C-Change, Institute for Economic and Racial Equity, The Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA.
Introduction: This mixed-method study sought to elucidate the impact of COVID-19 on the professional environments and career trajectories of midcareer research faculty in U.S. medical schools.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Biology, University of Puerto Rico-Rio Piedras, San Juan 00926, Puerto Rico
Despite significant strides in gender equity, the Nobel Prizes in STEM fields continue to exhibit glaring disparities in the recognition of women's contributions to science. Thirty years ago, only 3% of Nobel laureates in science were women; today, that number has increased marginally to 4%, raising the critical question: Why "still" so few? This opinion piece examines systemic inequities and structural barriers that hinder the equitable acknowledgment of women's and underrepresented groups' contributions to science. Data reveal that while women now comprise a significant proportion of degree recipients and workforce entrants in fields such as biomedical research and chemistry, their representation among Nobel laureates remains disproportionately low.
View Article and Find Full Text PDFBackground: Heart Failure (HF) quality of care (QoC) is associated with clinical outcomes. Therefore, we investigated differences in HF QoC across worldwide regions (with differing national income) and the association of quality indicators with outcomes.
Methods: We examined the quality of care (QoC) in acute heart failure (HF) patients across different regions using quality indicators (QIs) from the European Society of Cardiology (ESC) and the American Heart Association (AHA) to evaluate QoC.
JMIR Med Inform
January 2025
Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
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