The Factors Associated with The Triage Implementation in Emergency Department
Bibliographic record
Abstract
Introduction: Triage is defi ned as a process to sort patients based on the severity and emergency situation. In fact, Emergency Department (ED) in several hospitals in Indonesia do not implement it, so not all patients come to Emergency Department due to a true emergency case but there are also a false emergency. Implementing triage is important in order to decrease false emergency case and also increase ED service quality. The research goal was to analyze factors associated with the triage implementation in Emergency Department in Hospitals (type A and B). Methode: The research design was a cross sectional with corrrelative analysis. The research population was emergency department nurses and patients. Samples were taken by total sampling for the nurses (54 respondents) and accidental sampling for patients (54 respondents). The research instruments were questionnaire and direct observation. The research datas were analized using multivariat logistic regression by backward LR. Result: The result showed that the dominant factors correlated with the implementation of the triage was the performance factor (p value. 0,002), the patient factor (p value = 0.011), and the staffing factor (p value. 0.017). Discussion: The hospital management can increase the work motivation,then optimize the nurses by giving a job description clearly and improve nursing service quality through Triage Offi cer Course.Keywords: triage, performance factor, patient factor, staffi ng factor
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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 | 0.002 | 0.016 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".