Long emergency department length of stay: A concept analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".