K109) Assessment of Knowledge of Hospital Triage among Nurses in the Emergency Department of Zahedan University Hospitals
Bibliographic record
Abstract
Background:Triage in emergency departments is performed by nurses. In recent years, hospital triage has developed in Iran, and few studies have addressed nurses' competency in triage. Objective: The objective of this study was to assess the knowledge of nurses about triage in hospitals of Sistan-va-Balouchestan state in Iran.Methods:A survey was conducted among nurses in emergency departments (n = 10). The questionnaire was composed of factual knowledge questions about triage (n = 15) and triage decision-making questions (n = 10). Seventy nurses working in hospitals in Sistan-va-Balouchestan state participated. The questionnaire reliability was 0.60 using the test-re-test method. Content validity was considered based on Canadian Triage and Acuity Scale.Results:The response rate was 68% (70/102). Nurses proved to be unfamiliar with triage. Only 28% of their responses were correct. Only three emergency departments have specified special nurses to perform triage. Inter-rater agreement between nurses for all was r = 0.56 and for each nurse was r = 0.12.Conclusions:Emergency departments were not committed to a valid, reliable triage scale. Specialized education about hospital triage with a new approach is recommended. Further research on emergency department triage scales, standards, and guidelines 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".