Identifying the obstacles facing emergency nurses regarding treating CTAS1 and CTAS2 in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Emergency nurses play a pivotal role in delivering efficient emergency healthcare, yet they often encounter numerous challenges, especially while managing life-threatening cases, impacting both their well-being and patient satisfaction. This study seeks to identify the prevalent challenges faced by these nurses in Saudi hospitals when handling Canadian Triage and Acuity Scale (CTAS1 and CTAS2) cases, with the aim of mitigating or managing these issues in the future. METHODS: This study incorporated a mixed-method approach to identify obstacles in Emergency Department (ED) nursing treatment of CTAS1 and CTAS2 cases in two major Saudi Arabian hospitals. The research began with qualitative focus group interviews with expert ED nurses, followed by a quantitative survey to measure and explore relationships among the qualitative findings. Data analysis leveraged qualitative thematic analysis and principal component analysis, ensuring rigorous examination and validation of data to drive meaningful conclusions. FINDINGS: From expert interviews, key challenges for emergency nurses were identified, including resource management, communication, training compliance, and psychological factors. A survey of 172 nurses further distilled these into five major issues: patient care management, handling critical cases, administration support, patient care delay, and stress from patients' families. CONCLUSION: Through a mixed-method approach, this study pinpoints five pivotal challenges confronting emergency nurses in Saudi hospitals. These encompass difficulties in patient care management, the psychological toll of handling critical cases, inadequate administrative support, delays due to extended patient stays, and the stress induced by the presence of patients' families, all of which significantly impede emergency department efficiency and compromise nurse well-being.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".