Physicians' Earnings Do Not Affect Their Online Ratings
Bibliographic record
Abstract
Objective: Physician-rating websites have exploded in popularity in recent years. Consequently, these sites have garnered attention from researchers interested in factors influencing patient satisfaction. A doctor’s earnings might reflect practice patterns that could influence their patients’ perceptions. We sought to explore any association between physicians’ earnings and their online ratings. Methods: The names and billings of 500 physicians from British Columbia, Canada were randomly extracted from the 2016-17 BC Blue Book and matched to their profiles on RateMDs.com. Physicians’ earnings were compared to their global ratings and to their Staff, Punctuality, Helpfulness and Knowledge scores. Earnings and ratings were also compared between men and women. Results: We found no significant correlation between physicians’ earnings and their global online ratings (p=0.304). Weak negative correlations existed between earnings and Staff and Helpfulness ratings (Spearman’s rho = -0.055, p<0.001; rho = -0.033, p<0.028). Online ratings were largely favorable (mean MD rating of 3.85/5. Male physicians earned significantly more than their female colleagues ($371,734.85 and $261,590.82, respectively; p<0.001), but no significant difference existed between men and women with regards to online ratings (mean 3.87 and 3.81, respectively, p=0.191). Conclusions: No meaningful association was found between physicians’ earnings and their online ratings. Patients tend to review doctors favorably online; these data add to the discussion of whether male and female doctors are differentially rated. Trends towards increased transparency in health care systems may help to elucidate how doctors’ earnings influence patients’ perception of and satisfaction with the care they receive.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".