A comprehensive model for predicting burnout in Korean nurses
Bibliographic record
Abstract
BACKGROUND: Although burnout among nurses has been studied in a great deal, this work has not included Korean nurses. Furthermore, the role of personal resources such as empathy and empowerment in predicting the variance in burnout has never been examined. AIM: The purpose of this study was to understand the phenomenon of burnout among Korean nurses. A comprehensive model of burnout was examined to identify significant predictors among individual characteristics, job stress and personal resource, with the intention of providing a basis for individual and organizational interventions to reduce levels of burnout experienced by Korean nurses. METHODS: A cross-sectional correlational design was used. A sample of 178 nurses from general hospitals in southern Korea was surveyed from May 1999 to March 2000. The data were collected using paper and pencil self-rating questionnaires and analysed using descriptive statistics, Pearson correlations, and hierarchical multiple regression. RESULTS: Korean nurses reported higher levels of burnout than nurses in western countries such as Germany, Canada, the United Kingdom and the United States of America. Nurses who experienced higher job stress, showed lower cognitive empathy and empowerment, and worked in night shifts at tertiary hospitals were more likely to experience burnout. CONCLUSIONS: Identifying a comprehensive model of burnout among Korean nurses is an essential step to develop effective managerial strategies to reduce the problem. Suggestions to reduce the level of burnout include enhancing nurses' cognitive empathy and perceived power, providing clear job descriptions and work expectations, and exploring nurses' shift preferences, especially at tertiary hospitals. In future research we recommend recruiting nurses from broader geographical areas using random selection in order to increase the generalizability of the findings.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".