Understanding Gender Biases and Differences in Web-Based Reviews of Sanctioned Physicians Through a Machine Learning Approach: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Previous studies have highlighted gender differences in web-based physician reviews; however, so far, no study has linked web-based ratings with quality of care. OBJECTIVE: We compared a consumer-generated measure of physician quality (web-based ratings) with a clinical quality outcome (sanctions for malpractice or improper behavior) to understand how patients' perceptions and evaluations of physicians differ based on the physician's gender. METHODS: We used data from a large web-based physician review website and the Federation of State Medical Boards. We implemented paragraph vector methods to identify words that are specific to and indicative of separate groups of physicians. Then, we enriched these findings by using the National Research Council Canada word-emotion association lexicon to assign emotional scores to reviews for different subpopulations according to gender, gender and sanction, and gender and rating. RESULTS: We found statistically significant differences in the sentiment and emotion of reviews between male and female physicians. Numerical ratings are lower and sentiment in text reviews is more negative for women who will be sanctioned than for men who will be sanctioned; sanctioned male physicians are still associated with positive reviews. CONCLUSIONS: Given the growing impact of web-based reviews on demand for physician services, understanding the different dynamics of reviews for male and female physicians is important for consumers and platform architects who may revisit their platform design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".