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Record W3092347674 · doi:10.1016/j.ienj.2020.100930

Long emergency department length of stay: A concept analysis

2020· article· en· W3092347674 on OpenAlex
Jonas Andersson, Lena Nordgren, Ivy Cheng, Ulrica Nilsson, Lisa Kurland

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Emergency Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsCrowdingProxy (statistics)Emergency departmentSet (abstract data type)Computer sciencePsychologyMedical emergencyData scienceMedicineNursingCognitive psychologyMachine learning

Abstract

fetched live from OpenAlex

INTRODUCTION: Emergency Department (ED) Length of stay (LOS) has been associated with poor patient outcomes, which has led to the implementation of time targets designed to keep EDLOS below a specific limit. The cut-offs defining long EDLOS varies across settings and seem to be arbitrarily chosen. This study aimed to clarify the meaning of long EDLOS. METHODS: A concept analysis using the Walker and Avant approach was conducted. It included a literature search aiming to identify all uses of the concept, resulting in a set of defining attributes and a way of measuring the concept empirically. RESULTS: Long EDLOS was primarily used as proxy for other phenomena, e.g. boarding or crowding. The definitions had cut-offs ranging between 4 and 48 h. The attributes defining long EDLOS was waiting, a crowded ED environment and an inefficient organization. DISCUSSION: Time targets are probably more suitable when directed towards and tailored for specific sub-groups of the ED population.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.345
Teacher spread0.316 · 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