The effect of emotion regulation on empathic ability in Chinese nursing students: The parallel mediating role of emotional intelligence and self-consistency congruence.

AIM: This study aims to explore the influence of emotion regulation on empathic ability among undergraduate nursing students, as well as the mediating role of emotional intelligence and self-consistency congruence. DESIGN: A cross-sectional study was employed to examine the relationship between the emotion regulation and empathic ability in Chinese nursing students. METHODS: A total of 761 undergraduate nursing students were surveyed using the Interpersonal Reactivity Index (Chinese version), the Gross Emotion Regulation Questionnaire, Wang and Law's Emotional Intelligence Scale and the Self-Harmony Scale. RESULTS: There was a significant positive correlation between emotion regulation, empathic ability and self-harmony. Significant positive correlations were also found between emotion regulation, empathic ability and emotional intelligence. Mediation analysis revealed that self-harmony and emotional intelligence partially mediated the predictive relationship between emotion regulation and empathic ability, with self-harmony showing a more significant mediating effect. CONCLUSION: The findings suggest that emotion regulation among undergraduate nursing students indirectly influences their empathic ability through parallel mediating effects of self-harmony and emotional intelligence.

Development and Validation of a Prognostic Model to Predict High-Risk Patients for Coronary Heart Disease in Snorers With Uncontrolled Hypertension.

Purpose: Snoring or obstructive sleep apnea, with or without uncontrolled hypertension, is common and significantly increases the risk of coronary heart disease (CHD). The aim of this study was to develop and validate a prognostic model to predict and identify high-risk patients for CHD among snorers with uncontrolled hypertension. Methods: Records from 1,822 snorers with uncontrolled hypertension were randomly divided into a training set (n = 1,275, 70%) and validation set (n = 547, 30%). Predictors for CHD were extracted to construct a nomogram model based on multivariate Cox regression analysis. We performed a single-split verification and 1,000 bootstraps resampling internal validation to assess the discrimination and consistency of the prediction model using area under the receiver operating characteristic curve (AUC) and calibration plots. Based on the linear predictors, a risk classifier for CHD could be set. Results: Age, waist circumference (WC), and high- and low-density lipoprotein cholesterol (HDL-C and LDL-C) were extracted as the predictors to generate this nomogram model. The C-index was 0.720 (95% confidence interval 0.663-0.777) in the derivation cohort and 0.703 (0.630-0.776) in the validation cohort. The AUC was 0.757 (0.626-0.887), 0.739 (0.647-0.831), and 0.732 (0.665-0.799) in the training set and 0.689 (0.542-0.837), 0.701 (0.606-0.796), and 0.712 (0.615-0.808) in the validation set at 3, 5, and 8 years, respectively. The calibration plots showed acceptable consistency between the probability of CHD-free survival and the observed CHD-free survival in the training and validation sets. A total of more than 134 points in the nomogram can be used in the identification of high-risk patients for CHD among snorers with uncontrolled hypertension. Conclusion: We developed a CHD risk prediction model in snorers with uncontrolled hypertension, which includes age, WC, HDL-C, and LDL-C, and can help clinicians with early and quick identification of patients with a high risk for CHD.