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Full-Text Articles in Social and Behavioral Sciences
Acceptance And Commitment Therapy For A Child With Misophonia: A Case Study, Julie M. Petersen, Michael P. Twohig
Acceptance And Commitment Therapy For A Child With Misophonia: A Case Study, Julie M. Petersen, Michael P. Twohig
Psychology Faculty Publications
Misophonia, a condition involving hypersensitivity, anger, and/or disgust in response to specific noises (e.g., chewing, tapping), is highly underresearched in children. Several case studies point towards the utility of cognitive behavioral therapy and related treatments (e.g., acceptance and commitment therapy [ACT]). ACT presents a particularly promising option, as it focuses on building psychological flexibility in response to difficult internal experiences, rather than trying to remove or change them (e.g., responding effectively to irritation provoked by chewing). The present case study describes “Kelly” (pseudonym), a 12-year-old girl with moderately severe misophonia symptoms, who received a 16-session course of ACT for misophonia. …
Innovative Identification Of Substance Use Predictors: Machine Learning In A National Sample Of Mexican Children, Alejandro L. Vázquez, Melanie M. Domenech Rodríguez, Tyson S. Barrett, Sarah E. Schwartz Sarah.Schwartz@Usu.Edu, Nancy G. Amador Buenabad, Marycarmen N. Bustos Gamiño, María De Lourdes Gutiérrez López, Jorge A. Villatoro Velázquez
Innovative Identification Of Substance Use Predictors: Machine Learning In A National Sample Of Mexican Children, Alejandro L. Vázquez, Melanie M. Domenech Rodríguez, Tyson S. Barrett, Sarah E. Schwartz Sarah.Schwartz@Usu.Edu, Nancy G. Amador Buenabad, Marycarmen N. Bustos Gamiño, María De Lourdes Gutiérrez López, Jorge A. Villatoro Velázquez
Psychology Faculty Publications
Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e., 5th, 6th grade; Mage = 10.40, SDage = 0.82). Participants reported information on individual factors (e.g., gender, grade, religiosity, sensation seeking, self-esteem, perceived risk of substance use), socioecological factors (e.g., neighborhood quality, community type, peer influences, parenting), and …