Comparative Quality Indicators for Hospital Choice: Do General Practitioners Care?
Bibliographic record
Abstract
CONTEXT: The strategy of publicly reporting quality indicators is being widely promoted through public policies as a way to make health care delivery more efficient. OBJECTIVE: To assess general practitioners' (GPs) use of the comparative hospital quality indicators made available by public services and the media, as well as GPs' perceptions of their qualities and usefulness. METHOD: A telephone survey of a random sample representing all self-employed GPs in private practice in France. RESULTS: A large majority (84.1%-88.5%) of respondents (n = 503; response rate of 56%) reported that they never used public comparative indicators, available in the mass media or on government and non-government Internet sites, to influence their patients' hospital choices. The vast majority of GPs rely mostly on traditional sources of information when choosing a hospital. At the same time, this study highlights favourable opinions shared by a large proportion of GPs regarding several aspects of hospital quality indicators, such as their good qualities and usefulness for other purposes. In sum, the results show that GPs make very limited use of hospital quality indicators based on a consumer choice paradigm but, at the same time, see them as useful in ways corresponding more to the usual professional paradigms, including as a means to improve quality of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".