Symptom-level analysis of DSM-IV/DSM-5 personality pathology in later life: Hierarchical structure and predictive validity across self- and informant ratings

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

With various social pressures and the lack of knowledge about physical health, students have poor physical education quality and insufficient knowledge acquisition about physical health. Traditional physical health teaching is a process in which the teacher tells the theory of physical health and students passively accept it, which leads to physical health problems such as low learning efficiency of students' physical health knowledge and low interest in learning physical health knowledge. With the emphasis on physical health teaching and the development of technologies such as machine learning, machine learning is used to analyze the problems of physical health teaching and help students to learn physical health better to improve the efficiency of physical health teaching. The results of this paper show that the machine learning-based physical education and traditional physical education can reduce the injury rate of students' sports by 7.7% compared with traditional physical education, make students' interest in physical education and health learning reach 53.3%, and improve the efficiency of physical education and health learning. There is a degree of students' acquisition of physical health knowledge. The change from traditional physical health teaching ideology to machine learning-based physical education ideology can improve the teaching efficiency of physical health teaching, allow students to acquire more physical health knowledge, and effectively reduce the risk of students' injuries in sports.

A Hierarchical Taxonomy of Psychopathology (HiTOP) Primer for Mental Health Researchers

Mental health research is at an important crossroads as the field seeks more reliable and valid phenotypes to study. Dimensional approaches to quantifying mental illness operate outside the confines of traditional categorical diagnoses, and they are gaining traction as a way to advance research on the causes and consequences of mental illness. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a leading dimensional research paradigm that synthesizes decades of data on the major dimensions of psychological disorders. In this article, we demonstrate how to use the HiTOP model to formulate and test research questions through a series of tutorials. To boost accessibility, data and annotated code for each tutorial are included at OSF (https://osf.io/8myzw). After presenting the tutorials, we outline how investigators can use these ideas and tools to generate new insights in their own substantive research programs.