War Injuries and Nurses' Well-Being: Fatigue and Sleep Quality Among Critical Care Nurses in Najran Region, Saudi Arabia
Bibliographic record
Abstract
Introduction Critical care nurses must maintain optimal work performance. Fatigue and sleep disturbance can limit safe practice and cause negative patient outcomes. This study aimed to explore fatigue and sleep quality among critical care nurses in the war zone in Najran City, Saudi Arabia. Methods A cross-sectional research design was used and a convenience sample was applied to include 352 nurses working in critical units at various hospitals in Najran City, Saudi Arabia. A self-administered questionnaire containing three parts was used: demographic characteristics, the Pittsburgh Sleep Quality Index (PSQI), and the Fatigue Severity Scale (FSS). Results The study revealed that 232 nurses (65.9%) reported poor sleep quality. Regarding fatigue levels, 89 nurses (25.2%) reported severe fatigue and 113 (32.1%) reported moderate fatigue. Notably, caring for war-related injuries exhibited a significant positive correlation (r = 0.62, p = 0.0001). Experience correlated negatively (r = -0.47, p = 0.003) with sleep quality and fatigue scores. Most significantly, involvement in caring for war-related injuries showed a strong positive correlation (r = 0.71, p = 0.00001) with FSS scores. Conclusions Poor sleep quality was significantly widespread among the studied nurses. The results indicated that about one-quarter of the studied nurses reported severe fatigue, which was alarmingly prevalent among nurses. Nurses involved in caring for war-related injuries exhibited a strong positive correlation with both PSQI and FSS scores. Recommendations The authors recommend developing and implementing counseling and stress management programs to address the unique challenges faced by nurses caring for war-related injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".