Cardiovascular disease is a major complication of type 2 diabetes mellitus. In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social (...) information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve analysis was used to evaluate the model performance. Of participants, 272 were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. (shrink)

Transformational leadership has been becoming increasingly vital to the provision of high-quality health care, particularly during major public health emergencies. The present study aims to investigate the impact of transformational leadership on physicians’ performance and explore the cross-level underlying mechanisms with achievement motivations and coping styles among Chinese physicians. During 2017–2019, 1,527 physicians of 101 departments were recruited from six hospitals in China with a cluster random sampling method. Participants completed several questionnaires regarding their job performance, achievement motivations, coping styles, (...) and transformational leadership. Multilevel mediation effects were tested using cross-level path analysis. The result of this study indicated that transformational leadership was applied well in Chinese medical settings with a score of 101.56 ± 6.42. The hierarchical linear model showed that transformational leadership had a cross-level direct positive effect on physicians’ performance. Furthermore, results of cross-level path analyses revealed that transformational leadership contributed to physicians’ performance by sequentially influencing achievement motivations first and then coping styles. In addition, the path “transformational leadership → positive coping style → physicians’ performance” showed the strongest cross-level indirect effect. In summary, public health leaders should enhance physicians’ performance by promoting individual development, especially achievement motivation and PC style. (shrink)