Nursing Quality Indicators in Emergency Nursing
Bibliographic record
Abstract
BACKGROUND: Nursing quality indicators (NQIs) are essential for evaluating and managing care, yet few validated NQIs exist for emergency nursing. The dynamic nature of this field demands specific, validated indicators. PURPOSE: The purpose of this review was to identify NQIs in adult emergency nursing using Donabedian's quality categories (structure, process, outcome) and explore their validation. METHODS: A scoping review was conducted including articles from 2010 to February 2023, using the Cumulative Index to Nursing and Allied Health Literature and Medline (Ovid) databases. RESULTS: Among 936 screened articles, 18 were included, identifying 85 NQIs across structure (n = 14), process (n = 45), and outcome (n = 26) in emergency nursing. However, the validation of these NQIs was limited. CONCLUSIONS: NQIs evaluate emergency nursing quality, primarily in process assessment. Future work should validate the NQIs identified in this review for adult emergency nursing and search for potential new ones.
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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 | 0.040 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".