Background: Study engagement is regarded important to medical students' physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement.
View Article and Find Full Text PDFBackground: Medical school learning environment (MSLE) has a holistic impact on students' psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student's grade in the perception of MSLE.
View Article and Find Full Text PDFBackground: Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students' academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear.
View Article and Find Full Text PDFBackground: Studies exploring influencing factors of emotional engagement among medical students are scarce. Thus, we aimed to identify influencing factors of medical students' emotional engagement.
Methods: We carried out a multi-center cross-sectional study among 10,901 medical students from 11 universities in China.
Medical students' perceptions of the medical school learning environment (MSLE) have an important impact on their professional development, and physical and mental health. Few studies reported potential factors that influenced medical students' perceptions of MSLE. Thus, the main goal of this study was to identify influencing factors for medical students' perception levels of MSLE.
View Article and Find Full Text PDFPurpose: The purpose of this study was to construct a multi-center cross-sectional study to predict self-regulated learning (SRL) levels of Chinese medical undergraduates.
Methods: We selected medical undergraduates by random sampling from five universities in mainland China. The classical regression methods (logistic regression and Lasso regression) and machine learning model were combined to identify the most significant predictors of SRL levels.