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Record W2918869861 · doi:10.1097/ncq.0000000000000396

Associations Between Hospital-Level Patient Satisfaction Scores and Hospital-Acquired Pressure Ulcer Occurrences Among Medicare Stroke Patients

2019· article· en· W2918869861 on OpenAlexaff
Tamara Odom‐Maryon, Hsou Mei Hu, Huey‐Ming Tzeng

Bibliographic record

VenueJournal of Nursing Care Quality · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePatient satisfactionLogistic regressionEmergency medicineStroke (engine)MEDLINEFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Limited research has explored the associations between the US Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings data and hospital-acquired pressure ulcer (HAPU) occurrences. PURPOSE: We examined the associations between the hospital-level patient satisfaction HCAHPS scores with hospital care experience reported by Medicare patients 65 years or older and the occurrence of HAPUs among Medicare patients with stroke. METHODS: A matched case-control design was used. Patients with a history of stroke were identified using the 2011 Medicare fee-for-service patient data. Medicare Beneficiary Summary and Medicare Provider Analysis and Review files processed by the Chronic Conditions Data Warehouse were analyzed. Conditional logistic regression was used. RESULTS: HAPUs occur less frequently among Medicare patients with stroke who received inpatient care at hospitals with higher patient satisfaction HCAHPS scores for nurses' communication skills and quietness at night for the areas around patient rooms. CONCLUSIONS: Using hospital-level patient satisfaction HCAHPS scores to monitor and project HAPU occurrences is recommended.

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 score0.039
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.410
Teacher spread0.357 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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