Tian‐Yu Gou

AIMS AND OBJECTIVES: To investigate the interrelationships between workplace violence, thriving at work and turnover intention among Chinese nurses and to explore the action mechanism among these variables. BACKGROUND: Workplace violence is a dangerous occupational hazard globally, and it is pervasive in the health service industry. As a corollary, workplace violence may produce many negative outcomes among nursing staff. Consequently, it hinders nurses' professional performance and reduces nursing quality. DESIGN: A cross-sectional online survey was conducted. METHODS: A total of 1,024 nurses from 26 cities in China were recruited from February-May 2016. An anonymous questionnaire was used in this survey. Participants' completed data were collected using a demographics form and a 26-item questionnaire consisting of scales addressing workplace violence, thriving at work, job satisfaction, subjective well-being and turnover intention. To evaluate multivariate relationships, some multiple linear hierarchical regression analyses were performed. RESULTS: Workplace violence significantly negatively influenced nurses' job satisfaction and thriving at work, and significantly positively influenced nurses' turnover intention. Job satisfaction significantly predicted thriving at work and turnover intention. Job satisfaction not only fully mediated the relationship between workplace violence and thriving at work, but also partially mediated the relationship between workplace violence and turnover intention. Subjective well-being moderated the relationship between workplace violence and job satisfaction and the relationship between workplace violence and nurses' turnover intention. CONCLUSIONS: Adverse effects of workplace violence were demonstrated in this study. Decreases in job satisfaction were a vital mediating factor. The moderating effect of subjective well-being was helpful in reducing the harm of workplace violence to nurses and in decreasing their turnover intention. RELEVANCE TO CLINICAL PRACTICE: Workplace violence and its negative impact on nursing work should not go unnoticed by nursing managers. Nurses' subjective well-being is critical in controlling and mitigating the adverse effects of workplace violence.

Abstract. The selection of optimal physical parameterization schemes is a major source of uncertainty in WRF model simulations. A comprehensive evaluation of model performance requires simultaneous consideration of multiple output variables. However, existing multivariate approaches often rely on subjective or overly simplistic equal-weighting strategies and lack an objective mechanism to quantify variable importance. Such limitations can obscure significant biases in poorly simulated variables. To overcome this issue, this study proposes an objective dynamic weighting method for multivariate evaluation. The method employs a two-layer weighting framework based on two statistical metrics: the mean relative error, which measures the simulation accuracy of a variable, and the coefficient of variation of the absolute error, which reflects the sensitivity of a variable to the physical process under evaluation. The approach is applied and validated in assessing WRF parameterization schemes across two climatically distinct environments: the arid Northwest and the humid coastal Southeast of China. The results show that the method assigns greater weights to poorly simulated variables, such as precipitation and wind speed, thereby enabling the identification of more physically plausible and robust parameterization scheme combinations. Compared with the equal-weighting method, the scheme combinations obtained using this approach produce a lower Multivariate Integrated Evaluation Index (MIEI), a higher correlation coefficient (R), a lower Root Mean Square Error (RMSE), and exhibit superior performance in independent extreme-year validations. By dynamically incorporating both simulation performance and sensitivity specific to each variable, the method offers a more rigorous and objective framework for model evaluation and uncertainty reduction.