麻豆传媒

News — Results of a new Harrisburg University of Science and Technology study examining the role of gender in patients' perceptions of their doctors shows that reviews for female physicians are generally more informal and emotional than those for male physicians.

 The study, Understanding gender bias toward physicians using online doctor reviews, is co-authored by Harrisburg University Ph.D. student Sonam Gupta and Dr. Kayla Jordan, assistant professor of social analytics at Harrisburg University.  The study appears in the Psychology of Language and Communication 2022, Vol. 26, No. 1.

 "We know that gender stereotypes can impact the doctor-patient relationship but identifying bias can be difficult. Text analytics gives us a way to explore the impact of gender in these relationships. Using online patient reviews, we find those female doctors are reviewed more on their interpersonal skills, and reviews of female doctors contain more references to their gender. If we are to mitigate bias, we need robust, varied ways to identify bias across contexts," says Gupta.

 The researchers note that gender bias continues to be an ongoing issue in the field of medicine. While bias may come in many forms, patients’ biases and perceptions have been understudied and may impact adherence to treatment, leading to unequal outcomes. Online reviews for doctors are a naturalistic way to study gender bias. In this study, the researchers leveraged the LIWC psychological linguistic analysis tool to analyze the language styles of ZocDoc and RateMDs reviews and understand the potential role of gender in patients’ perceptions of their doctors. 

 "While our study was exploratory, the results suggest that both patients and physicians need to increase their awareness of how their biases may be affecting how they give and receive vital health information," says Jordan.

 Harrisburg University offers a 36-credit Ph.D. program in Data Science.  The mission of the program is to create scientifically minded and technically proficient professionals with a comprehensive background in the methodological diversity of the data sciences and the intellectual depth to offer influential perspectives to analytical teams across disciplines.  More information on the program is found at

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