Burnout and depression among nurses in Japan and China: the moderating effects of job satisfaction and absence
Bibliographic record
Abstract
This study focuses on the relationships between emotional exhaustion and other dimensions of burnout as well as depression among nurses in Japan and China. Attitudinal and behavioral moderators as coping mechanisms are suggested to mitigate the effect of emotional exhaustion on depersonalization, diminished personal accomplishment and depression. More specifically, we analyze the alleviating effect of absence and the moderating effect of job satisfaction as a compensatory coping mechanism. Data were collected from 239 nurses in Japan and 550 nurses in mainland China. The study used existing measures with appropriate translations. The instruments exhibited satisfactory psychometric properties for both samples. Descriptive statistics, correlation, and hierarchical moderated regression using both two-way and three-way interactions were employed to analyze the data. Job satisfaction and absence were found to moderate the relationship between emotional exhaustion and depression simultaneously among both Japanese and Chinese nurses. Job satisfaction and absence simultaneously moderated the effect of emotional exhaustion on diminished personal accomplishment among Japanese nurses only. The theoretical role of job satisfaction and absence in alleviating the detrimental effects of emotional exhaustion, and their practical significance for healthcare in general, and for the management of nurses in Japan and China in particular are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".