This paper lays out the research motives, methodology, outcome measures, and outcomes of a randomized effectiveness trial, performed in 10 outpatient clinical service organizations in two states, Massachusetts and Hawaii, from 2005 to 2009.
Decades of randomized controlled trials have produced separate evidence-based treatments for depression, anxiety, and conduct problems in youth, but these treatments are not often used in clinical practice, and they produce mixed results in trials with the comorbid, complex youths seen in practice. In this paper, the authors suggest that an integrative, modular redesign may help. They compared standard/separate and modular/integrated arrangements of evidence-based treatments for depression, anxiety, and conduct problems in youth with usual care treatment, with the modular design permitting a multi-disorder focus and a flexible application of treatment procedures. A total of 84 community clinicians were randomly assigned to one of three conditions for the treatment of 174 clinically referred youths who were seven to 13 years of age. Interventions included: standard manual treatment; cognitive behavioral therapy for depression or anxiety, and behavioral parent training for conduct problems; modular treatment; integrating the procedures of the 3 separate treatments; and usual care. The authors conclude that the modular approach outperformed usual care and standard evidence-based treatments on multiple clinical outcome measures and suggest that the modular approach may be a promising way to build on the strengths of evidence-based treatments, improving their utility and effectiveness with referred youths in clinical practice settings. Publisher Abstract Provided
Downloads
Similar Publications
- A Meta-analysis of the Outcomes of Bullying Prevention Programs on Subtypes of Traditional Bullying Victimization: Verbal, Relational, and Physical
- Hot Spots Policing as Part of a City-wide Violent Crime Reduction Strategy: Initial Evidence from Dallas
- Enhancing Corporate Crime Enforcement with Machine Learning—A Multidisciplinary Risk Factor Approach