Workplace violence against hospital healthcare workers in China: a national WeChat-based survey
Bibliographic record
Abstract
BACKGROUND: Workplace violence (WPV) is a serious issue for healthcare workers and leads to many negative consequences. Several studies have reported on the prevalence of WPV in China, which ranges from 42.2 to 83.3%. However, little information is available regarding the correlates of WPV among healthcare workers and the differences across the different levels of hospitals in China. This study aimed to explore the correlates of WPV and career satisfaction among healthcare workers in China. METHODS: A self-designed WeChat-based questionnaire was used that included demographic and occupational factors. The Chinese version of the Workplace Violence Scale was used to measure WPV. Career satisfaction was assessed using two questions about career choices. Descriptive analyses, chi-square tests and multivariate logistic regressions were used. RESULTS: A total of 3706 participants (2750 nurses and 956 doctors) responded to the survey. Among the 3684 valid questionnaires, 2078 (56.4%) reported at least one type of WPV in the last year. Multivariate logistic regressions revealed that male sex, shift work, bachelor's degree education, a senior professional title, working more than 50 h per week and working in secondary-level hospitals were risk factors associated with WPV. Healthcare workers who had experienced higher levels of WPV were less likely to be satisfied with their careers. CONCLUSIONS: WPV remains a special concern for the Chinese healthcare system. Interventions to reduce WPV should be implemented by health authorities to create a zero-violence practice environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".