Public reporting on quality, waiting times and patient experience in 11 high-income countries
Bibliographic record
Abstract
This article maps current approaches to public reporting on waiting times, patient experience and aggregate measures of quality and safety in 11 high-income countries (Australia, Canada, England, France, Germany, Netherlands, New Zealand, Norway, Sweden, Switzerland and the United States). Using a questionnaire-based survey of key national informants, we found that the data most commonly made available to the public are on waiting times for hospital treatment, being reported for major hospitals in seven countries. Information on patient experience at hospital level is also made available in many countries, but it is not generally available in respect of primary care services. Only one of the 11 countries (England) publishes composite measures of overall quality and safety of care that allow the ranking of providers of hospital care. Similarly, the publication of information on outcomes of individual physicians remains rare. We conclude that public reporting of aggregate measures of quality and safety, as well as of outcomes of individual physicians, remain relatively uncommon. This is likely to be due to both unresolved methodological and ethical problems and concerns that public reporting may lead to unintended consequences.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".