Nurses Psychological Well-Being During Covid19 Outbreak in Saudi Arabia
Bibliographic record
Abstract
Background: The global coronavirus disease pandemic of 2019 (COVID-19) has caused healthcare provider to experience extraordinary psychological stress. Objective: This study assessed thepsychological well-being of nurses during the COVID-19 outbreak and factors associated with it.Methods: An online survey was sent to all nurses working at the Ministry of Health Hospitals andliving in Tabuk city, Saudi Arabia. A total of 219 nurses were completed the survey. The Depression,Anxiety and Stress Scale – 21 items (DASS-21) assessed the psychological well-being of respondentsin the previous week. Results: One -quarter of nurses (24.7%) reported extremely severe symptoms ofanxiety, more than one third (37%) reported extremely sever symptoms of stress, less than one quarter(14.1%) reported extremely sever symptoms of depression. Higher anxiety scores were significantlyassociated with direct contact with confirmed COVID 19 cases (p= 0.08), general health status (p=0.001) and marital status (p= 0.042). Higher DASS-21 Stress scores were significantly associated withworking more than eight hours per shift (p=0.024), marital status(P=0.036) and general health status(p <0.001). Higher DASS-21 Depression scores was significantly associated general health status (p<0.001). Conclusions & implication for practice: The COVID-19 outbreak has had a significant effect on thepsychological well-being of Saudis nurses, particularly nurses who were married, had contact withCOVID 19 cases, had working more than eight hours per shift, and had poor general health status.Protecting the psychological health of nursing staff is essential, nursing leaders are in charge of providingsocial support for nurses so that they will be able to cope with their anxiety, stress, and depression.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".