ABSTRACT
OBJECTIVE: To explore the mediating effect of the general self-efficacy(GSE), stress coping personality(SCP) and perceived professional benefits(PPB) among nursing practice students. METHODS: A total of 836 nursing interns from six grade A hospitals in six cities were selected as the research subjects using convenience sampling method. The GSE, SCP and PPB were investigated by the General Self-Efficacy Scale, Scale of Stress Coping Personality for College Students and Questionnaire of Nurses Perceived Professional Benefit. RESULTS: The average scores of GSE, SCP, and PPB were(24.6±5.8),(183.1±28.7) and(139.5±18.0), respectively. The scores of GSE and SCP were positively correlated with that of PPB [correlation coefficients(r) were 0.31 and 0.38 respectively, both P<0.01], and a positive correlation was found between GSE and SCP(r=0.41, P<0.01). The hierarchical regression results showed that the sense of control, tenacity and tolerance of SCP of the interns had a predictive effect on their PPB(all P<0.05); but the effect of SCP on PPB was weakened after inclusion of GSE(P<0.01). The structural equation model analysis results showed that both SCP and GSE of interns had a direct positive predictive effect on PPB(all P<0.01), GSE played a partial mediating role between SCP and PPB, accounting for 20.3% of the total effect. CONCLUSION: The SCP of nursing interns can directly or indirectly affect their PPB, and GSE plays a partial mediating effect between SCP and PPB.
ABSTRACT
Objective To compare the Bayesian statistics and the classical statistics in the quantile regression analysis in order to select a more effective method .Methods The large sample data was chosen , and the QUANTREG procedure in SAS was used for the classical statistics and the MCMC procedure in SAS for the Bayesian one , respectively .Using ten-fold cross-validation method , the goodness of fitting of the models was appraised in terms of the fitted effect based on the training dataset and the predicted effect based on the predictive dataset .Results In most cases, the indexes of the quantile regression models in the classical statistics were slightly worse than those of the Bayesian one .In the ten-fold cross-validation of the partial samples as a training dataset , the fitting effect of the lower quartile ( Q1 ) and upper quartile ( Q3 ) of the Bayesian statistics was better than that of the classical one .However , the median ( Q2 ) fitting effect of the Bayesian statistics was slightly worse than that of the classical one .As for the prediction effect , the Bayesian statistical quantile regression model was superior to the classic one .Conclusion To expect high accuracy , such as the predictive effects and fitting effects of each quantile , the Bayesian quantile regression analysis should be chosen .If the major concern is the fitting effect of the median , careful selection from the approaches mentioned above is needed .If time and energy are limited, and the sample size is large enough , the classic statistical quantile regression analysis is a good choice .