The Effects of a Triaging Educational Training Program on Triaging Knowledge, Self-efficacy and Accuracy in Emergency Department Nurses in a Private Hospital in Saudi Arabia
Bibliographic record
Abstract
Inaccurate triage decisions can lead to increased length of patient stays in the emergency department, increased rehospitalizations, and increased risk of mortality, all of which are quality of care indicators (Srinivas et al., 2021). The triaging decisions in the emergency department at our facility are conducted by relatively new and inexperienced nurses. Anecdotal evidence from the emergency department leadership suggests that inaccurate triaging decisions are being made. In addition, 60 percent of a limited sample of 30 charts that were recently reviewed contained inaccurate triaging decisions. The accuracy of these triaging decisions has not been systematically evaluated at our facility. Triaging educational training programs that include systematic audits of triaging decisions have been shown to improve the sustained accuracy of triaging decisions by improving nurses’ triaging knowledge and triaging confidence or self-efficacy (Oh & Jung, 2024). The purpose of this project is to implement the Canadian Triage and Acuity Scale (CTAS) triaging educational training program (Bullard et al., 2017) for emergency room nurses and to evaluate the effectiveness of the program in improving nurses’ triaging knowledge, triaging self-efficacy, and the accuracy of triaging decisions. The triaging educational training program is currently being implemented at our facility and includes demographic characteristics of the participants and a triaging knowledge (Phykubuye et al., 2019) and self-efficacy assessment (Oh and Jung, 2024 & Bandura, 2006) before participating in the training and four months later. An expert triage nurse will conduct a chart audit, using the Canadian Triage and Acuity Scale, that measures the accuracy of the triaging decisions made by the nurses before and after participating in the triaging educational training program.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".