This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2410) Physician and Health Services Rating by Consumers (128) Machine Learning (1465) Natural Language Processing (692) Validity, Reliability, and Bias (93)

Published on in Vol 6, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34902, first published .
Understanding Gender Biases and Differences in Web-Based Reviews of Sanctioned Physicians Through a Machine Learning Approach: Mixed Methods Study

Understanding Gender Biases and Differences in Web-Based Reviews of Sanctioned Physicians Through a Machine Learning Approach: Mixed Methods Study

Understanding Gender Biases and Differences in Web-Based Reviews of Sanctioned Physicians Through a Machine Learning Approach: Mixed Methods Study

Authors of this article:

Julia Barnett1 Author Orcid Image ;   Margrét Vilborg Bjarnadóttir2 Author Orcid Image ;   David Anderson3 Author Orcid Image ;   Chong Chen2 Author Orcid Image
Article Authors Cited by (2) Tweetations (9) Metrics

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  1. Moura L, Jones D, Sheikh I, Murphy S, Kalfin M, Kummer B, Weathers A, Grinspan Z, Silsbee H, Jones L, Patel A. Implications of Large Language Models for Quality and Efficiency of Neurologic Care. Neurology 2024;102(11) View
  2. Sehgal N, Rader B, Brownstein J. Examining the Role of Physician Characteristics in Web-Based Verified Primary Care Physician Reviews: Observational Study. Journal of Medical Internet Research 2024;26:e51672 View