Pediatric Nurses' Turnover Intention and Its Association with Calling in China's Tertiary Hospitals
Bibliographic record
Abstract
PURPOSE: To examine the turnover intention of Chinese pediatric nurses, its influential socio-demographic factors, and the association with calling and job satisfaction. DESIGN AND METHODS: We randomly surveyed 10% of the nurses from 50% of the children's tertiary hospitals nationwide in China. Data were collected on nurses' turnover intention and associated factors such as age, income, skill level, working years, job satisfaction, and calling in 2017. RESULTS: In total, 547 nurses were surveyed, and the response rate was 98.6%. More than a third of pediatric nurses had the intention to quit their current jobs. Influential factors associated with turnover intention included position, skill level, calling, and job satisfaction. Low job satisfaction of administration, workload, relationships with colleagues, work itself, and remuneration and benefits were negatively associated with turnover intention, with the odds ratio of high turnover intention in the lowest level of satisfaction ranging from 2.0-7.8 when compared with the medium level. However, calling was the strongest factor influencing turnover intention, and a weak calling may increase the risk of high turnover intention more than ten times, after adjusting for job satisfaction. Job satisfaction may partially mediate the relationship between calling and turnover intention. CONCLUSION: The turnover intention of nurses was high in Chinese pediatric tertiary hospital. Calling may be the strongest influential factor of turnover intention. PRACTICE IMPLICATIONS: To alleviate pediatric nurses' turnover rate, it may be helpful to develop interventions to increase job satisfaction and calling.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".