Scope, Breadth, and Differences in Online Physician Ratings Related to Geography, Specialty, and Year: Observational Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Physician ratings websites have emerged as a novel forum for consumers to comment on their health care experiences. Little is known about such ratings in Canada. OBJECTIVE: We investigated the scope and trends for specialty, geographic region, and time for online physician ratings in Canada using a national data source from the country's leading physician-rating website. METHODS: This observational retrospective study used online ratings data from Canadian physicians (January 2005-September 2013; N=640,603). For specialty, province, and year of rating, we assessed whether physicians were likely to be rated favorably by using the proportion of ratings greater than the overall median rating. RESULTS: In total, 57,412 unique physicians had 640,603 individual ratings. Overall, ratings were positive (mean 3.9, SD 1.3). On average, each physician had 11.2 (SD 10.1) ratings. By comparing specialties with Canadian Institute of Health Information physician population numbers over our study period, we inferred that certain specialties (obstetrics and gynecology, family practice, surgery, and dermatology) were more commonly rated, whereas others (pathology, radiology, genetics, and anesthesia) were less represented. Ratings varied by specialty; cardiac surgery, nephrology, genetics, and radiology were more likely to be rated in the top 50th percentile, whereas addiction medicine, dermatology, neurology, and psychiatry were more often rated in the lower 50th percentile of ratings. Regarding geographic practice location, ratings were more likely to be favorable for physicians practicing in eastern provinces compared with western and central Canada. Regarding year, the absolute number of ratings peaked in 2007 before stabilizing and decreasing by 2013. Moreover, ratings were most likely to be positive in 2007 and again in 2013. CONCLUSIONS: Physician-rating websites are a relatively novel source of provider-level patient satisfaction and are a valuable source of the patient experience. It is important to understand the breadth and scope of such ratings, particularly regarding specialty, geographic practice location, and changes over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".