Relationship Between Shift Work and Personality Traits of Nurses and Their Coping Strategies
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Because of social progress, population growth, industrialization, and the requirements of some jobs, a significant percentage of employees are working in shifts. Shift work is considered a threat to health that could have unfavorable effects on various aspects of human life. This study investigated the relationship between shift work and the personality traits of nurses and their coping strategies in a selection of non-governmental hospitals in Tehran in 2014. METHODS: This applied cross-sectional descriptive research employed the Standard Shift work Index and Eysenck Personality Questionnaire (EPQ) which, after confirmation of its validity and reliability (Cronbach's alpha 0.73), were distributed among 305 nurses from 6 non-governmental hospitals in Tehran selected through cluster random sampling. Data was analyzed in two statistical levels: descriptive and inferential. RESULTS: Results revealed that 43.6% of the nurses participating in the study were introverted and 56.4% were extroverted. There are significant relationships between age and physical health (P=0.008), sex and physical health (P=0.015), educational level and physical health (P=0.014), sex and cognitive, somatic anxiety (P=0.006), age and social-family status (P=0.001), marital status and social-family status (P=0.001), having a second job and social-family status (P=0.001), educational level and sleep and fatigue (P=0.002), work experience and coping strategies (P=0.044), and sleep and fatigue and personality traits (P=0.032). CONCLUSION: Complying with the standards of working hours for nurses and avoiding overtime when scheduling, especially for nurses with more work experience, can prevent the severe complications of shift work, enhance health, and ultimately enhance the quality of care. By improving the physical, psychological, and social health of nurses, the quality of patient care can be expected to improve, too.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".