Browsing by Subject "gender stereotypes"

In three essays, this dissertation examines how patients judge their physicians in online physician reviews and whether those judgements align with traditional gender stereotypes. Specifically, I qualitatively explore patients’ judgments of their physicians’ interpersonal manner and technical competence, and the predominant factors within the two dimensions. I then train a machine-learning algorithm to code patients’ judgments in online physician reviews at scale. Finally, I use the machine-coded sample to analyze physician gender differences in judgments received from patients and how those judgments affect physicians’ review star ratings. In Essay 1, I propose an elaborated theoretical framework to identify the predominant factors underlying patients’ interpersonal manner and technical competence judgments of their physicians. This framework expands on prior grounded theory work by Lopez et al. (2012) and uses findings from a qualitative content analysis of 2,000 reviews received by distinct physicians. For this framework, I draw on a larger, new dataset of physician reviews from Healthgrades.com, one of the leading physician review websites, and use a balanced sample of reviews representing primary care physicians and surgeons, male and female physicians, and low- and high-rated reviews. I provide rich descriptions and illustrative quotations of the factors comprising interpersonal manner and technical competence, and describe factors added to and removed from Lopez et al.’s original framework. This framework from Essay 1 demonstrates that patients value their physicians on a wide array of interpersonal manner and technical competence factors, including but not limited to bedside manner, going above and beyond, availability, knowledge, diagnostic skill, and open-mindedness about treatment. In Essay 2, I train, test, and validate an advanced natural language processing algorithm called Robustly Optimized BERT Pre-Training Approach (i.e., RoBERTa) for classifying the presence and positive or negative valence of patients’ interpersonal manner and technical competence judgments in online physician reviews. I use the 2,000 manually coded physician reviews from Essay 1 to train and test two classification models, one for interpersonal manner and one for technical competence. Both models perform with 90% accuracy, with high precision, recall, and weighted F1 scores. I validate the models using the full sample of 345,053 RoBERTa-coded reviews for 167,150 physicians by testing associations between the valence-coded judgments and review star ratings and by comparing review rating and gender analyses with extant results in the literature. The fine-tuned algorithm from Essay 2 allows us to code a large dataset of unstructured textual review data with high efficiency and accuracy, enabling subsequent large-scale text analysis. In Essay 3, I analyze whether patients’ judgments of their physicians’ interpersonal manner and technical competence align with traditional gender stereotypes. Drawing on the Stereotype Content Model, I hypothesize that patients’ judgments will conform with gender stereotypes, such that female physicians will be more likely to receive reviews with interpersonal manner judgments whereas male physicians will be more likely to receive reviews with technical competence judgments. Using the full sample of machine-coded reviews from Essay 2, I estimate multilevel logistic regressions to identify gender differences in interpersonal manner and technical competence judgments of physicians. Results from Essay 3 suggest that patients’ judgments partly align with traditional gender stereotypes: Female physicians are more likely to receive interpersonal manner judgments, but male physicians are not more likely to receive technical competence judgments. Whether female physicians are relatively more likely to receive praise or criticism for their interpersonal manner depends on their specialty. In stereotypically warm specialties, like primary care, females are penalized for seeming cold, whereas in stereotypically technical specialties, like surgery, females are advantaged for appearing warm. Last, female physicians, in some cases, are either not rewarded as much or penalized more than their male counterparts in their star ratings when receiving positive or negative interpersonal manner and technical competence judgments.