Association Between Web-Based Physician Ratings and Physician Disciplinary Convictions: Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Physician rating websites are commonly used by the public, yet the relationship between web-based physician ratings and health care quality is not well understood. OBJECTIVE: The objective of our study was to use physician disciplinary convictions as an extreme marker for poor physician quality and to investigate whether disciplined physicians have lower ratings than nondisciplined matched controls. METHODS: This was a retrospective national observational study of all disciplined physicians in Canada (751 physicians, 2000 to 2013). We searched ratings (2005-2015) from the country's leading online physician rating website for this group, and for 751 matched controls according to gender, specialty, practice years, and location. We compared overall ratings (out of a score of 5) as well as mean ratings by the type of misconduct. We also compared ratings for each type of misconduct and punishment. RESULTS: There were 62.7% (471/751) of convicted and disciplined physicians (cases) with web-based ratings and 64.6% (485/751) of nondisciplined physicians (controls) with ratings. Of 312 matched case-control pairs, disciplined physicians were rated lower than controls overall (3.62 vs 4.00; P<.001). Disciplined physicians had lower ratings for all types of misconduct and punishment-except for physicians disciplined for sexual offenses (n=90 pairs; 3.83 vs 3.86; P=.81). Sexual misconduct was the only category in which mean ratings for physicians were higher than those for other disciplined physicians (3.63 vs 3.35; P=.003). CONCLUSIONS: Physicians convicted for disciplinary misconduct generally had lower web-based ratings. Physicians convicted of sexual misconduct did not have lower ratings and were rated higher than other disciplined physicians. These findings may have future implications for the identification of physicians providing poor-quality care.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".