Gender Disparities in Citations and Altmetric Attention Score in Oncology
Year: 2025
Type: article
Abstract: PURPOSE: Altmetric Attention Score (AAS) is a measure of the quantity of attention that a scholarly work receives, and evidence about gender gaps in AAS in oncology is lacking. Our objective was to analyze potential disparities in the AAS within oncology by comparing research publications authored by women first and last authors with those authored by men. Secondarily, we aimed to quantify the extent of over-/undercitation by gender. MATERIALS AND METHODS: The initial data set was compiled from the Altmetric database through Application Programming Interface (API) using oncology-related search terms. Author gender categories were assigned on the basis of the Gender Guesser API. For example, those with first and last authors labeled woman were categorized as woman first author/woman last author (WW). Over-/undercitation was calculated using observed citations and expected citations. Analyses were completed both for the oncology literature as a whole and for prominent subspecialty peer-reviewed journals. RESULTS: < .01 for both). Although the proportion of WW authors in oncology publications increased over time, the man first author/man last author combination was overcited (mean citation percentage difference [MCD] = +16.2%), whereas WW was undercited (MCD = -7.7%). There was variation in both proportion of WW papers and over-/undercitation among oncologic subspecialties. CONCLUSION: Significant gender disparities in citation rates and AAS exist across various fields within oncology. This highlights a systemic issue where woman-authored research is undercited and receives less attention compared with man-authored work, with the potential to affect career advancement, funding opportunities, and academic recognition.
Source: JCO Oncology Practice
DOI: 10.1200/op-24-00767
Authors: Rebecca A. Campbell, Emma Helstrom, Lauren Chew, Renu Eapen, Elizabeth Plimack, Andres Correa, Maarten Albersen, Alexander Kutikov, Philip Abbosh, Adam Calaway, Amanda Nizam, Shilpa Gupta, Sarah Psutka, Pedro Barata, Nazli Dizman, Mohit Sindhani, Christopher Weight, Georges Haber, Laura Bukavina
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: Bladder Cancer Advocacy Network
OpenAlex ID: W4409318101
