Influence of nursing interns’ resilience on burnout: Mediating effects of well-being
Bibliographic record
Abstract
Objective: This study examined the relationship between well-being, psychological resilience, and burnout among nursing interns in China.Methods: A cross-sectional study was carried out at 2 tertiary hospitals in Guangdong Province, China. Data were collected from 360 nursing interns using a structured questionnaire, and structural equation modeling was used to analyze the correlation between well-being, resilience, and burnout.Results: Participants’ burnout score was in the upper range (mean [M]=12.748, standard deviation [SD] = 6.654). Burnout was negatively correlated with resilience (r = -0.477, p < .01) and well-being (r = -0.573, p < .01). Well-being mediated the relationship between resilience and burnout.Conclusions: Resilience and well-being are inversely correlated with burnout, and well-being mediates the relationship between resilience and burnout. Improving well-being can reduce burnout risk and improve resilience among nursing interns. To prevent burnout among nursing interns, nursing managers should aim to improve their well-being by optimizing the work environment, promoting the cohesion of the nursing team, actively guiding and providing necessary help, and supporting the development of each intern’s nursing career.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".