The Impact of Practice Environment and Resilience on Burnout among Clinical Nurses in a Tertiary Hospital Setting
Bibliographic record
Abstract
The purpose of this study was to examine practice environment, resilience, and burnout and to identify the impacts of practice environment and resilience on burnout among clinical nurses working at a tertiary hospital. A cross-sectional secondary data analysis was conducted using a convenience sample of 199 nurses. The nurses completed survey questionnaires regarding practice environment, resilience, and burnout. The majority of the nurses were below the age of 30, single, and worked in medical-surgical wards. Approximately, 92% of the nurses reported moderate to high burnout, with a mean practice environment score of 2.54 ± 0.34 and resilience score of 22.01 ± 5.69. Practice environment and resilience were higher in the low level of burnout than in the moderate to high level of burnout. After controlling for demographic and occupational characteristics, resilience and nursing foundations for quality of care were significant predictors of burnout (OR = 0.71, p = 0.001; OR = 0.01, p = 0.036, respectively), explaining 65.7% of the variance. In a mixed practice environment, increased resilience and nursing foundations for quality of care lowered nurses’ burnout. Our findings suggest that interventions focused on enhancing individual resilience and practice environment and building better nursing foundations for quality of care should be developed and provided to alleviate burnout in clinical nurses working at tertiary hospitals. Nursing and hospital administrators should consider the importance of practice environment and resilience in nurses in developing interventions to decrease burnout.
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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 | 0.005 |
| 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 | 0.002 |
| 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".