Google star ratings of Canadian hospitals: a nationwide cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Data on patients' self-reported hospital experience can help guide quality improvement. Traditional patient survey programmes are resource intensive, and results are not always publicly accessible. Unsolicited online hospital reviews are an alternative data source; however, the nature of online reviews for Canadian hospitals is unknown. METHODS: We conducted a nationwide cross-sectional study of Canadian acute care hospitals with more than 10 Google Reviews during the 2018-2019 fiscal year. We characterised the volume and distribution of Google Reviews of Canadian hospitals, and assessed their correlation with hospital characteristics (teaching status, size, occupancy rate, length of stay, resource utilisation) and Canadian Patient Experience Survey on Inpatient Care (CPES-IC) scores. RESULTS: 167 out of 523 (31.9%) acute care hospitals in Canada met the inclusion criteria. Among included hospitals, there was a total of 10 395 Google Reviews and a median of 35 reviews per hospital. The mean Google Star Rating for included hospitals was 2.85 out of 5, with a range of 1.36-4.57. Teaching hospitals had significantly higher mean Google Star Ratings compared with non-teaching hospitals (3.16 vs 2.81, p <0.01). There was a weak, positive correlation between hospitals' Google Star Ratings and CPES-IC 'Overall Hospital Experience' scores (p =0.04), but no significant correlation between Google Star Ratings and other hospital characteristics or subcategories of CPES-IC scores. INTERPRETATION: There is significant interhospital variation in patients' self-reported care experiences at Canadian acute care hospitals. Online reviews can serve as a readily accessible source of real-time data for hospitals to monitor and improve the patient experience.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".