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Surveying Patients as a Start to Quality Improvement in the Surgical Suites Holding Area

2005· article· en· W2323494402 on OpenAlexaff
Joanna Bailey, Lynne McVey, Anna Pevreal

Bibliographic record

VenueJournal of Nursing Care Quality · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsDistractionActive listeningQuality (philosophy)Patient satisfactionSet (abstract data type)MedicineMedical emergencyOperations managementPsychologyMedical educationNursingComputer scienceEngineering

Abstract

fetched live from OpenAlex

The holding area, as the patient's first introduction to the surgical suites, has the potential to set the tone for the entire surgical experience. To identify targets for improvement efforts in the holding area, a convenience sample of 51 surgical patients completed a 12-item patient satisfaction survey developed using Androfact before discharge from hospital. Results reveal 5 aspects that fall below the desired benchmark satisfaction rate of 80%: staff holding personal conversations in the patients' presence, being offered distraction materials while waiting, pleasantness of the physical environment, reassurance that family members would be kept up-to-date during the surgical procedure, and comfort to provide personal information without worrying that others were listening. Discussion of findings indicates priorities for improvement efforts in the holding area.

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)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.263
GPT teacher head0.558
Teacher spread0.295 · 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 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

Citations11
Published2005
Admission routes1
Has abstractyes

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