Effects of job stress and coping behaviour on job satisfaction in Korean male nurses
Bibliographic record
Abstract
Purpose : The purpose of this study was to investigate the effects of job stress and coping behaviour on job satisfaction in Korean male nurses. Methods : The structured questionnaires were used for the survey of 73 male nurses who worked in hospitals located in B and S City in South Korea. To measure job stress, we used a Korean job stress scale. To measure coping behaviour, we used an instrument developed by Han & Oh (1990), and for job satisfaction, an instrument developed by Kim (2010). The data were analysed using PASW 18.0 (Chicago, USA), including descriptive statistics, independent sample t-tests, and ANOVAs. Results : The results showed mean job stress score of 3.27, mean coping behaviour score of 3.14 and mean job satisfaction score of 3.28 (all scores on a 5-point scale). Job satisfaction was positively correlated with coping behaviour ( r = 0.426, p < .001), but was not correlated with job stress. In addition, job satisfaction was higher among subjects with coping behaviour scores in the highest quartile than among those with coping behaviour scores in the lowest quartile ( t = -2.881, p = .007). Conclusion: Coping behaviour was found to be a relevant factor influencing the job satisfaction of male nurses. Thus, an environment and workplace culture that allows male nurses to develop and apply their coping ability must be cultivated in order to promote job satisfaction.
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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 | 0.002 |
| 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".