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Record W1986231566 · doi:10.1097/nna.0000000000000042

Improving the Patient Experience

2014· article· en· W1986231566 on OpenAlexaff
Christina Dempsey, Barbara Reilly, Nell Buhlman

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsDempsey (Canada)
Fundersnot available
KeywordsPatient experienceIncentivePurchasingNursingValue-Based PurchasingPaymentMedicinePatient careHealth carePsychologyBusinessMarketing

Abstract

fetched live from OpenAlex

Patients spend more time with nurses during an admission than with any other profession in the hospital. Nurses and their interactions with patients are central to shaping and improving the patient's experience. Patient experience, as measured by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, drives 30% of value-based purchasing (VBP) scores and incentive payments, as prescribed under the Patient Protection and Affordable Care Act. Hospital performance on the communication with nurses' domain within HCAHPS predicts performance on several other domains. In addition, nurses at the bedside have significantly lower engagement scores than nurses who are not involved in direct patient care. Considering the relationship between nurse engagement and patient experience and the relationship between patient experience and hospital success under VBP, pursuing strategies and tactics that will foster and sustain nurse engagement is critical for nurse executives.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.106
GPT teacher head0.456
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations66
Published2014
Admission routes1
Has abstractyes

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