Using the Five-Level Taiwan Triage and Acuity Scale Computerized System: Factors in Decision Making by Emergency Department Triage Nurses
Bibliographic record
Abstract
Triage classifies and prioritizes patients' care based on the acuity of the illness in emergency departments (EDs). In Taiwan, the five-level Taiwan Triage and Acuity Scale (TTAS) computerized system was implemented nationally in 2010. The purpose of this study was to understand which factors affect decision-making practices of triage nurses in the light of the implementation of the new TTAS tool and computerized system. The qualitative data were collected by in-depth interviews. Data saturation was reached with 16 participants. Content analysis was used. The results demonstrated that the factors affecting nurses' decision making in the light of the newly implemented computerized system sit within three main categories: external environmental, patients' health status, and nurses' experiences. This study suggests ensuring the patient's privacy while attending the triage desk, improving the critical thinking of triage nurses, and strengthening the public's understanding of the ED visits. These will make ED triage more efficient.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".