Considering the potential unintended consequences of RateMDs: an exploratory study in one specialty
Bibliographic record
Abstract
Background: Websites that facilitate communication between patients regarding their experiences with individual physicians are now relatively commonplace. Given patient-generated ratings are publicly available, physicians could use these to access rarely available patient feedback. We explored the content of reviews associated with low physician ratings and consider the potential benefits and consequences of relying on this form of freely available data to support individual life-long learning. Methods: We conducted an exploratory qualitative descriptive study. We collected narrative comments associated with low numerical ratings on one physician-rating website (RateMDs) drawn from one specialty in Canada. Written reviews associated with low numerical ratings (≤2/5) for Canadian otolaryngologists were collected yielding a total of 878 comment sets that were analyzed deductively and iteratively. Results: We found that patient comments described poor performance in areas that aligned, for the most part, with the CanMEDS roles including Professional, Communicator, and Leader; specifically referring to management of the clinical environment, administrative staff, and trainees. Conclusion: While not intended for physician feedback, physicians could access patient-to-patient ratings and associated written reviews as a means to identify areas of practice improvement. However, this represents an unintended use of these websites. While speculative, access to patient-to-patient rating websites could negatively impact physician confidence or self-worth - representing a negative consequence of their use. The utilization of these data for potential self-improvement represents an unintended use of patient-to-patient ratings and so may be accompanied by unintended consequences for physicians who use these data as potential feedback, and patients who contribute to physician rating sites.
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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 | 0.028 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".