The causes, consequences, and mediating effects of job burnout among hospital employees in Taiwan
Bibliographic record
Abstract
For the purpose of explaining the causes, consequences and mediating effects of burnout on relevant variables, the researcher conducted a cross-sectional survey of 371 hospital employees in Taiwan. Four principal findings are made. First, with respect to the three components of burnout experienced by hospital employees, the most frequently reported is emotional exhaustion, being also the most problematic among hospital employees compared with employees in other industries. Second, while increased workload coupled with role conflict increases the likelihood of burnout among hospital employees, improved work autonomy and social support reduce its likelihood. Next, the study finds a direct correlation between employees’ perceptions of low levels of emotional exhaustion and depersonalization and high levels of organizational commitment. In contrast, employees’ perceptions of high levels of emotional exhaustion and depersonalization lead to high turnover intention. Finally, the result of the hierarchical regression analysis demonstrates a partial mediating effect of burnout in the current study. These findings suggest the need for hospital management to improve their wellbeing and incentive strategies, to embark upon regular investigations into job burnout and to adopt appropriate measures to meet the professional development needs of hospital employees.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".