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Record W2344720963

Registered nurses and discharge planning in a Taiwanese ED: A neglected issue?

2016· article· en· W2344720963 on OpenAlexaff
Wen Chang, Suzanne Goopy, Chun‐Chih Lin, Alan Barnard, Hsueh‐Erh Liu, Chin‐Yen Han

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDischarge planningWorkloadEmergency departmentNursingPerspective (graphical)PsychologyPerceptionQualitative researchMedicineMedical educationSociologyManagementComputer science
DOInot available

Abstract

fetched live from OpenAlex

Published research on discharge planning is written from the perspective of hospital wards and community services. Limited research focuses on discharge planning in the emergency department (ED). The objective of this study was to identify ED nurses’ perceptions of factors influencing the implementation of discharge planning. This qualitative study collected data from 25 ED nurses through in-depth interviews and a drawing task in which participants were asked to depict on paper the implementation of discharge planning in their practice. Factors influencing discharge planning were grouped into three categories: discharge planning as a neglected issue in the ED, heavy workload, and the negative attitudes of ED patients and their families. The study highlighted a need for effective discharge planning to be counted as an essential clinical competency for ED nurses and factored into their everyday workload. Nurses perceived that organizational culture, and parents’ and relatives’ attitudes were barriers to implementing discharge teaching in the ED.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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