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Record W2260700832 · doi:10.1371/journal.pone.0147296

Comparative Quality Indicators for Hospital Choice: Do General Practitioners Care?

2016· article· en· W2260700832 on OpenAlexaff
Marie Ferrua, Claude Sicotte, Benoît Lalloué, Étienne Minvielle

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsQuality (philosophy)Global Positioning SystemGovernment (linguistics)Context (archaeology)Health careMedicineBusinessMarketingComputer scienceGeographyTelecommunicationsPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.222
GPT teacher head0.482
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2016
Admission routes1
Has abstractyes

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