Work‐family conflict and job burn‐out among Chinese doctors: the mediating role of coping styles
Bibliographic record
Abstract
BACKGROUND: Burn-out among doctors threatens their own health, and that of their patients. To identify risk factors of the doctor burn-out is vital to improving their health and increasing the quality of healthcare services. This study aims to explore the relationship between work-family conflict (WFC) and burn-out among Chinese doctors and the mediating role of coping styles in this relationship. METHODS: A cross-sectional survey was conducted in China, with a questionnaire packet which consisted of the Chinese Maslach Burnout Inventory (CMBI), WFC Scale and the Simplified Coping Style Questionnaire (SCSQ). A total of 2530 doctors participated in the survey. Correlation analysis was performed to explore the relationship between CMBI, WFC and SCSQ scores. A linear regression model was set to determine the mediating role of coping styles on the relationship between WFC and burn-out. RESULTS: Doctors who had higher scores on work interfering with family conflict, reported more emotional exhaustion (r=0.514, P<0.001) and had a sense of accomplishment (r=-0.149, P<0.001). Simultaneously, family interfering with work (FIW) was positively associated with all dimensions of burn-out (r=0.213, 0.504, 0.088, respectively, P<0.001). Coping styles had partial, complete and even mediating effects on the relationship between WFC and burn-out among Chinese doctors. CONCLUSIONS: WFC was correlated with burn-out, and coping style was a mediator in this relationship among Chinese doctors. Coping style was a positive resource against burn-out.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".