Resilience and work-related stress among mental health nurses in Jeddah, Saudi Arabia
Bibliographic record
Abstract
Work-related stress is harmful physical and emotional reactions that can occur when there is a conflict between an employee’s work needs and his or her level of control to meet those demands. Work-related stress can come from multiple causes, however, when stress occurs in unmanageable amounts for the nurse, mental and physical changes may occur. Resilience gives people the mental toughness to deal with stress, trauma, adversity, and difficulties. The study aimed to explore the level of work-related stress among mental health care nurses and their resilience capacity at Eradah & Mental Health Complex–Eradah Services (EMHC-ES) in Jeddah, Saudi Arabia. A descriptive, correlational, cross-sectional research design was utilized taking the responses of 172 nurses. The questionnaire encompassed the Mental Health Professional Stress Scale (MHPSS) 42 items assessed on a four-point Likert scale and the Connor Davidson Resilience Scale (CD-RISC-10) 10-items assessed on a 5-point Likert scale. The results revealed a satisfactorily high level of resilience (M = 30.7 on a scale of 0-40) and a moderate level of stress among the participants (M = 1.86 on a scale of 0 to 3) with the mean subscale score was the highest for the workload associated stress and the lowest for the homework conflict-related stress. There was a near to significant correlation between the stress level and the resilience among the participants (p = .053). An appropriate strategy in health care organizations to assess stress, explore causes, and provide early management in mental health care settings is highly recommended.
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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 | 0.001 |
| 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 | 0.001 |
| 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".