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Record W2340524463 · doi:10.1177/2374373515615977

Correlation of Inpatient Experience Survey Items and Domains With Overall Hospital Rating

2015· article· en· W2340524463 on OpenAlexafffundabout
Kyle Kemp, Brandi McCormack, Nancy Chan, Maria Santana, Hude Quan

Bibliographic record

VenueJournal of Patient Experience · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersAlberta Health Services
KeywordsScale (ratio)Health careRating scaleContext (archaeology)Patient experienceCorrelationNursingTelephone interviewHospital dischargePsychologyNursing careInpatient careMedicineFamily medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine which individual patient experience questions and domains were most correlated with overall inpatient hospital experience. METHODS: Within 42 days of discharge, 27 639 patients completed a telephone survey based upon the Hospital-Consumer Assessment of Healthcare Systems and Processes instrument. Patients rated their overall experience on a scale of 0 (worst care) to 10 (best care). Correlation coefficients were calculated to assess the relationships between individual survey questions and domains with overall experience. RESULTS: Questions on provider coordination and nursing care were most correlated with overall experience. Hospital cleanliness, quietness, and discharge information questions showed poor correlation. Correlation with overall experience was strongest for the "communication with nurses" domain. CONCLUSIONS: Our individual question results are novel, while the domain-based findings replicate those of US-based providers, results which had not yet been reported in the Canadian context-one with universal health care coverage. Our results suggest that our large health care organization may attain initial inpatient experience improvements by focusing upon personnel-based initiatives, rather than physical attributes of our hospitals.

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)0.001
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 score0.095
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.404
Teacher spread0.326 · 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

Citations22
Published2015
Admission routes3
Has abstractyes

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