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Record W4414680766 · doi:10.3928/00220124-20250630-01

Effect of a Triage Educational Intervention on Nurses' Knowledge and the Efficiency of Urgent Care in Saudi Arabia

2025· article· en· W4414680766 on OpenAlexaboutno aff
Nashi M. Alreshedi, Afaf Mufadhi Alrimali, Shaykhah M. Alreshidi, Debora Tabungar, Kristine Angeles Gonzales

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTriageIntervention (counseling)MEDLINEHealth careProgram evaluationKnowledge level

Abstract

fetched live from OpenAlex

Background: Emergency department overcrowding affects patient safety and service efficiency. Although the Canadian Triage and Acuity Scale (CTAS) is widely implemented in Saudi Arabia, variability in triage accuracy remains as a result of limited formal training. This study assessed the impact of a structured CTAS educational intervention on nurses' triage knowledge and the performance of a hospital-based urgent care center. Method: A quasi-experimental pretest/posttest design was used. Fifteen nurses at a high-volume urgent care center completed a 1-day CTAS Proficiency Training Course. A 15-item scenario-based questionnaire measured knowledge before and after training. The urgent care center time-based indicators (registration-to-triage, triage-to-decision, and total length of stay) were extracted for 1 month before and after the intervention, covering 33,720 patient visits. Data were analyzed with the Mann–Whitney U and Wilcoxon signed-rank tests. Results: Knowledge scores improved significantly ( p = .015). All of the urgent care center time intervals decreased ( p < .001), with CTAS Level II waiting times dropping from 33 to 9 minutes. Conclusion: The CTAS training enhanced nurse triage knowledge and improved urgent care efficiency. Structured triage education is recommended.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.359
Teacher spread0.354 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2025
Admission routes1
Has abstractyes

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