Hemodialysis nurse burnout in 31 provinces in mainland China: A cross‐sectional survey
Bibliographic record
Abstract
Abstract Introduction Job burnout is an occupational psychological syndrome with a high prevalence among nurses in China. Hemodialysis (HD) nursing work has the characteristics of high intensity, high technical content, and high risk. The aims of this study were to investigate the prevalence and level of job burnout among HD nurses in China and explore the potential factors associated with burnout among HD nurses. Methods This was a cross‐sectional study in 2019. Survey data were collected from 2738 HD centers in mainland China. Job burnout was measured by the Chinese version of the Maslach Burnout Inventory. The working atmosphere, interpersonal relationships with colleagues, and intention to leave were each assessed by a single question respectively. Multiple linear regression and structural equation modeling were used for the analysis. Findings A total of 10,570 surveys were collected. A total of 1199 (11.34%) HD nurses reported a high level of emotional exhaustion, 782 (7.40%) reported a high level of depersonalization, and 6767 (64.02%) reported a low level of personal accomplishment. Job burnout in the Northeastern region of mainland China was higher than that in other regions (p < 0.05). The working atmosphere, interpersonal relationships, region, hospital level, educational level, career planning, age, and number of children were significantly associated with burnout among HD nurses (p < 0.001, adjusted R2 = 0.313). The working environment, individual factors, and specialist nurse training were significantly associated with HD nurse burnout and intention to leave (comparative fit index = 0.907; goodness of fit index = 0.930; root mean square error of approximation = 0.055). Discussion There were notable regional differences in the burnout of HD nurses. This study contributes to the knowledge of the possible relationship of job burnout and intention to leave in HD nurses. It is suggested that improving the working atmosphere or interpersonal relationships and providing more training opportunities can alleviate job burnout in HD nurses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".