ObjectiveTo compare attitudes of nursing undergraduates to patients with mental illness before and after learning psychiatric nursing course and provide evidence for the improvement of teaching of the course. MethodsStigma scale to patients with mental illness was used as a questionnaire for collecting data, which was conducted on nursing undergraduates taking psychiatric nursing course between March and June 2012. Paired t-test was used to compare the differences between students' attitudes before and after learning the course. ResultsSignificant difference was found for danger factor (P<0.05), while there was no statistically significant difference in the social isolation factor and social ability factor before and after the psychiatric nursing course (P>0.05). ConclusionPsychiatric nursing course has a great influence on students' evaluation of the danger of patients with mental illness which reduces the discrimination against the patients.
ObjectiveTo compare and evaluate the discrimination, validity, and reliability of different data envelopment analysis (DEA) models for measuring the effectiveness of models by selecting different input and output indicators of the model.MethodsData from health statistical reports and pilot program of diagnosis-related groups of tertiary hospitals in Hubei Province from 2017 to 2018 were used to analyze the discrimination, content and structure validity, and reliability of the models. Six DEA models were established by enriching the details of input and output on the basis of the input and output indicators of the conventional DEA model of hospitals.ResultsFrom the view of discrimination, the results of all models were left-skewed, the cost-efficiency model had the lowest left-skewed degree (skewness coefficient: -0.14) and was the flattest (kurtosis coefficient: -1.02). From the view of structure validity, the results of the cost-efficiency model were positively correlated with total weights, outpatient visits, and inpatient visits (r=0.328, 0.329, 0.315; P<0.05). From the perspective of content validity, the interpretation of model was more consistent with theory of production after revision of input and output indicators. From the view of reliability, the cost efficiency model had the largest correlation coefficient between the data of 2017 and 2018 (r=0.880, P<0.05).ConclusionsAfter refining the input and output indicators of the DEA model, the discrimination, validity, and reliability of the model are higher, and the results are more reasonable. Using indicators such as discrimination, validity, and reliability can measure the effectiveness of the DEA model, and then optimize the model by selecting different input and output indicators.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.