Importance: Guided internet-delivered cognitive behavioral therapy (i-CBT) is a low-cost way to address high unmet need for anxiety and depression treatment. Scalability could be increased if some patients were helped as much by self-guided i-CBT as guided i-CBT.
Objective: To develop an individualized treatment rule using machine learning methods for guided i-CBT vs self-guided i-CBT based on a rich set of baseline predictors.
Background And Aims: DSM-5 includes Internet gaming disorder (IGD) as a condition for further study. While online and offline gaming may produce undesired negative effects on players, we know little about the nosology of IGD and its prevalence, especially in countries with emerging economies.
Methods: A self-administered survey has been employed to estimate prevalence of DSM-5 IGD and study the structure and performance of an instrument in Spanish to measure DSM-5 IGD among 7,022 first-year students in 5 Mexican universities that participated in the University Project for Healthy Students (PUERTAS), part of the World Health Organization's World Mental Health International College Student Initiative.
Introduction: The present study analyzes the main barriers and adaptations to brief interventions that focus on addictive behavior treatments carried out in clinical settings by 756 health professionals during their adoption process in 350 Primary Attention Units in Mexico.
Method: A descriptive cross-sectional study was conducted and consisted in the application of an instrument that explored diverse aspects, such as knowledge about evidence based brief intervention (BI) programs, barriers during the execution, and adaptations of the BI.
Results: the main barriers were related to the implementation of sessions and the user's characteristics such as educational level.