There needs to be serious transformation of evidence-based interventions (EBIs) into real-world solutions; otherwise, EBIs will never achieve the intended public health impact. In a randomized trial, we reported effects of a redesigned anxiety program. Herein, we described the redesign process that led to the program.
View Article and Find Full Text PDFJ Clin Child Adolesc Psychol
November 2020
In 1998, Ost published [One-session treatment of specific phobias-a rapid and effective method] [in Swedish] giving rise to the idea that brief, intensive, and concentrated psychosocial interventions could exhibit public health impact. At this juncture, and per criteria of the Society for Clinical Child and Adolescent Psychology, there are data supporting that brief, non-pharmacological intervention [prescriptions] for pediatric anxiety can be considered well-established or probably efficacious. In addition, data from 76 randomized controlled trials ( = 17,203 youth) yield an overall mean effect size of 0.
View Article and Find Full Text PDFThere is a need to optimize the fit between psychosocial interventions with known efficacy and the demands of real-word service delivery settings. However, adaptation of evidence-based interventions (EBI) raises questions about whether effectiveness can be retained. This randomized controlled trial (RCT) evaluated a streamlined package of cognitive, behavior, and social skills training strategies known to prevent and reduce anxiety symptom and disorder escalation in youth.
View Article and Find Full Text PDFAnxiety disorders are among the most common psychiatric problems in youth, fail to spontaneously remit, and place some youth at risk for additional behavioral and emotional difficulties. Efforts to target anxiety have resulted in evidence-based interventions but the resulting prevention effects are relatively small, often weakening over time. Mobile health (mHealth) tools could be of use to strengthen the effects of anxiety prevention efforts.
View Article and Find Full Text PDFTwin factor mixture modeling was used to identify temperament profiles while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (M = 7.4 years, SD = .84; 49% female; 88.
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