ABSTRACT
In the past, researchers have used various data sources like social media, admission applications, or letters of recommendation to identify gender-based differences in linguistic data. One such avenue in healthcare is the online physician reviews website. Such websites, for example, RateMyDoctors.com, ZocDoc, or even Yelp.com have become a go-to place for patients when choosing their physicians. In the current research, we used two different natural language processing (NLP) approaches: semi-supervised and unsupervised topic modeling to analyze the text of the reviews to identify gender-based linguistic differences from patients’ perspectives. We found that female physicians receive more reviews on their personable skills and warmth, aligning with the Stereotype Content Model. We also found other popular topics discussing bedside manners and overall patient experiences, where the reviews suggested that patients were happier with their experience with female physicians and perceived them to have more positive traits than their male counterparts. Although our study did not reflect significant linguistic differences; it highlights the importance for patients and doctors to be more aware of potential gender stereotypes and perceptions.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Citations for data availability are included in the manuscript. Code can be found at https://github.com/sonamgupta1105/patient_Reviews_analysis. Studies involved existing, publicly available data and were approved by the IRB at Harrisburg University of Science and Technology.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10410236.2024.2343467.