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Enigma of social media use: complexities of social media addiction through the serial mediating effects of emotions and self-presentation.
Front Psychol
December 2024
Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Introduction: Excessive social media use, though considered unhealthy, is no longer formally categorized as an addiction or disorder, leading to a lack of consensus on this behavior. It raises concerns regarding the exclusion of Internet Addiction Disorder from the DSM-5-TR due to insufficient empirical evidence. This study investigates the serial mediating effects of positive and negative affect, fear of missing out, and offline and online self-presentation in the relationship between social media use and social media addiction.
View Article and Find Full Text PDFPreferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment.
BMC Public Health
November 2024
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Introduction: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial for effective control. We previously developed MySTIRisk, an artificial intelligence-based risk tool that predicts the risk of HIV and STIs. We examined the attributes that encourage potential users to use it.
View Article and Find Full Text PDFBest Practice in Using Social Media: The European Association of Urology Position Statement.
Eur Urol
October 2024
Urology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy. Electronic address:
This European Association of Urology position statement provides a comprehensive overview and recommendations on best practices for the use of social media by urologists and other health care professionals involved in urology.
View Article and Find Full Text PDFEnough but not too many: A bi-threshold model for behavioral diffusion.
PNAS Nexus
October 2024
Department of Mathematics and Statistics, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, Ontario, Canada L2S 3A1.
Behavioral diffusion is commonly modeled with the linear threshold model, which assumes that individuals adopt a behavior when enough of their social contacts do so. We observe, however, that in many common empirical settings individuals also appear to abandon a behavior when too many of their close contacts exhibit it. The bi-threshold model captures this tendency by adding an upper threshold, which, when exceeded, triggers behavioral disadoption.
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