What Strategies Do the Nurses Apply to Cope With Job Stress?: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Nursing staff encounter a lot of physical, psychological and social stressors at work. Because the adverse effects of job stress on the health of this group of staff and subsequently on the quality of care services provided by nurses; study and identify how nurses cope with the job stress is very important and can help prevent the occurrence of unfavorable outcomes. OBJECTIVES: The aim of this study was to explore the experiences of nurses to identify the strategies they used to cope with the job stress. METHODS: In this qualitative study content analysis approach was used. Purposive sampling approach was applied. The sample population included 18 nurses working in three hospitals. Data collection was conducted through face to face unstructured interview and was analyzed using conventional content analysis approach. FINDINGS: The analysis of the data emerged six main themes about the strategies used by nurses to cope with job stress, which, include: situational control of conditions, seeking help, preventive monitoring of situation, self-controlling, avoidance and escape and spiritual coping. CONCLUSIONS: Exploring experiences of nurses on how to cope with job stress emerged context-dependent and original strategies and this knowledge can pave the ground for nurses to increase self-awareness of how to cope with job stress. And could also be the basis for planning and the adoption of necessary measures by the authorities to adapt nurses with their profession better and improves their health which are essential elements to fulfill high-quality nursing care.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".