<p>Utilization of an Electronic Triage System by Emergency Department Nurses</p>
Bibliographic record
Abstract
INTRODUCTION: Emergency departments use triage systems to prioritize patients according to the severity of their condition. The Electronic Canadian Triage and Acuity Scale (E-CTAS) is a popular system that categorizes patients into five levels to manage patient flow and prioritize patient access to health-care services. METHODS: We assessed the factors that influence E-CTAS usage in emergency departments in Eastern Saudi Arabia. Seventy-one nurses were included from two emergency departments that adopted E-CTAS. We used the technology acceptance model (TAM) to assess the influencing factors. The TAM was reliable in the study setting (Cronbach's α = 0.87). RESULTS: All of the TAM domains were significantly related to the usage of E-CTAS: perceived ease of use, perceived usefulness, importance of training, social influence, behavior intention, and attitude. We also showed that E-CTAS use significantly increased with years of experience and training. DISCUSSION: Many factors influenced the use of this electronic triage system. Focusing on these factors in future electronic triage system implementations might increase the hospital staff's compliance, thus improving accuracy and better organizing the patient flow in emergency departments.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".