Knowledge and Skills in Triage Assessment among Nurses in Emergency Department Hospital Saudi Arabia
Bibliographic record
Abstract
Introduction: Triage is the nursing process and the first step for patients who came to or were brought to the Emergency Department (ED). As hospitals have more patients, the ED becomes overcrowded with non-emergency patients. An inaccurate triage decision not only delays patient treatment but also incurs unnecessary expenses for the facility. Patients in the waiting area should be reassessed to determine their conditions. Treatment for patients on a first-come, first-served basis without regard to the triage acuity level of their illnesses for severity, may result in delays for critically ill patients. The rationale of triage in the ED is to prioritize incoming patients and identify those who need to be seen first. Methods: Descriptive study using cross-sectional questionnaires for non-random sampling. Quantitative approaches were chosen as the study type. Questionnaires were completed by 41 of the selected sample size. The aim of the study is to determine the level of knowledge and skills in triage assessment among nurses in Emergency Department Hospitals in Saudi Arabia. Results: Response rates of 100% were obtained from the study. 19 (46.3%) of the participants were aged between 31-40 years old. Most of the respondents have Bachelor’s in nursing which are 38 (92.7%). Respondents with experience in ED of about 11-20 years are 18 (43.9%) of the total. 100% of respondents say that they are familiar with the Canadian Triage and Acuity Scale (CTAS) triage primary assessment. 100% will change the CTAS level when the patient deteriorates, and 100% agreed to give Paracetamol to the patient with a body temperature of more than 38 degrees. Seven skill level questions were completed with 100% correct answers. There is no significant relationship between triage knowledge and triage skill Conclusion: This finding indicates that there is a high level of knowledge and skill in triage assessment. Hence high knowledge and good skill should be maintained by continuing education, in-service, and training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".