The effectiveness of Multisystemic Therapy (MST): A meta-analysis

Stouwe, Trudy van der; Asscher, Jessica J.; Stams, Geert Jan; Deković, Maja; Laan, P.H. van der

doi:10.1016/j.cpr.2014.06.006

Cited by 238 publications

(149 citation statements)

References 43 publications

Supporting

Mentioning

103

Contrasting

Unclassified

Order By: Relevance

“…In this case, including that single discrete variable as a moderator in the syntax (i.e., after the mods~element) would suffice. However, it has become rather common to report on the mean effect (as well as significance and confidence interval) of all categories of a discrete potential moderating variable (see, for instance, Assink et al, 2015;Houben et al, 2015;Rapp, Van den Noortgate, Broekaert, & Vanderplasschen, 2014;Van der Hallen, Evers, Brewaeys, Van den Noortgate, & Wagemans, 2015;Van der Stouwe, Asscher, Stams, Dekovic, & Van der Laan, 2014;Weisz et al, 2013 # Determine the potential moderating effect of publication status; # Unpublished studies are now tested against published # studies, so published studies serve as the reference category; # Print the results stored in the object "published" on screen. published <-rma.mv(y, v, mods =~pstatnotpub, random = list(~1 | effectsizeID,1 | studyID), tdist=TRUE, data=dataset) summary(published, digits=3)…”

Section: Categorical Moderators With Three Categoriesmentioning

confidence: 99%

Fitting three-level meta-analytic models in R: A step-by-step tutorial

Assink¹,

Wibbelink²

2016

TQMP

733

846

View full text Add to dashboard Cite

Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3) calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing categorical and continuous moderator analyses; and (6) examining a multiple moderator model. By example, the authors demonstrate how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models can be easily extended so that the potential moderating influence of variables can be examined.

show abstract

Section: Categorical Moderators With Three Categoriesmentioning

confidence: 99%

Fitting three-level meta-analytic models in R: A step-by-step tutorial

Assink¹,

Wibbelink²

2016

TQMP

733

846

View full text Add to dashboard Cite

show abstract

“…These covariates were hypothesized to be related to either selection into treatment condition or to our outcome variables, based on previous research (West et al 2014; van der Stouwe et al 2014). Variables included youth d emographic characteristics such as a youth’s age at referral to MST treatment, gender (0 = male, 1 = female), and race (0 = white, 1 = non-white), and several case characteristics including number of prior episodes of out-of-home care, number of placements settings, history of maltreatment (0 = no, 1 = yes), history of adjudication (0 = no, 1 = yes), history of juvenile training school placement (0 = no, 1 = yes), and case assignment prior to or during MST treatment (0 = child welfare, 1 = juvenile probation).…”

Section: Methodsmentioning

confidence: 99%

“…The positive effects of MST in addressing problem behaviors among high-risk populations of offending youth have led to program adaptations for other at-risk populations of youth and families, including those with substance abuse problems (Henggeler et al 1999, 2006), serious conduct problems (Asscher et al 2013; Weiss et al 2013), serious emotional disturbance (Stambaugh et al 2007; Rowland et al 2005), and youth and families involved in the child protective services (MST-Child Abuse and Neglect or MST-CAN; Swenson et al 2010). The benefits of MST to reduce problem behaviors among high-risk youth and families have been corroborated by two meta-analytic studies, which suggest small to moderate treatment effect sizes on internalizing and externalizing symptoms among youth who participated in MST (van der Stouwe et al 2014; Curtis et al 2004; for a rebuttal, see; Littell et al 2005). In sum, there is a strong empirical evidence supporting MST as an effective intervention to improve functioning across a range of behavioral outcomes for youth in high-risk populations.…”

Section: Introductionmentioning

confidence: 90%

Placement and Delinquency Outcomes Among System-Involved Youth Referred to Multisystemic Therapy: A Propensity Score Matching Analysis

Vidal

Steeger

Caron

et al. 2017

Adm Policy Ment Health

View full text Add to dashboard Cite

Multisystemic therapy (MST) was developed to help youth with serious social, emotional, and behavioral problems. Research on the efficacy and effectiveness of MST has shown positive outcomes in different domains of development and functioning among various populations of youth. Nonetheless, even with a large body of literature investigating the treatment effects of MST, few studies have focused on the effectiveness of MST through large-scale dissemination efforts. Utilizing a large sample of youth involved in a statewide dissemination of MST (n = 740; 43% females; 14% Black; 29% Hispanic; 49% White; Mage = 14.9 years), propensity score matching was employed to account for baseline differences between the treatment (n = 577) and comparison (n = 163) groups. Treatment effects were examined based on three outcomes: out-of-home placement, adjudication, and placement in a juvenile training school over a 6-year period. Significant group differences remained after adjusting for baseline differences, with youth who received MST experiencing better outcomes in offending rates than youth who did not have an opportunity to complete MST due to non-clinical or administrative reasons. Survival analyses revealed rates of all three outcomes were approximately 40% lower among the treatment group. Overall, this study adds to the body of literature supporting the long-term effectiveness of MST in reducing offending among high-risk youth. The findings underscore the potential benefits of taking evidence-based programs such as MST to scale to improve the well-being and functioning of high-risk youth. However, strategies to effectively deliver the program in mental health service settings, and to address the specific needs of high-risk youth are necessary.

show abstract

“…Twenty-five evaluations of MST have been published and provide evidence that MST can produce short-and long-term reductions in criminal behavior and out-of-home placements for serious juvenile offenders (van der Stouwe et al, 2014). In a long-term follow-up, it led to 54 percent fewer arrests and 57 percent fewer days incarcerated for 22 years following the program (Wagner et al, 2014).…”

Section: Juveniles: Family Functional Therapy and Multisystemic Therapymentioning

confidence: 99%

Leveraging Research and Philanthropy to Reduce Crime and Violence in the Mississippi Delta

Saunders¹,

Barnes-Proby²,

Peterson³

2018

View full text Add to dashboard Cite

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest.RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org For more information on this publication, visit www.rand.org/t/RR2457Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2018 RAND CorporationR® is a registered trademark.iii PrefaceIn fall 2017, the Walton Family Foundation requested that the RAND Corporation perform research to help the foundation's understanding of how to use philanthropy to positively impact public safety in Phillips County, Arkansas and Coahoma County, Mississippi. Specifically, foundation members were interested in (1) the current state of criminal activity and public safety, as well as the experiences and priorities of the community residents; (2) the institutional challenges and unmet needs of the local criminal justice systems agencies; (3) the variability of crime across the counties; (4) which best practices and approaches could help improve safety in the counties; and (5) research-based recommendations for how the Walton Family Foundation could improve public safety. We developed a project to address these questions and conducted research and analysis between fall 2017 and spring 2018. This research report describes both the methods and findings of the research to help the Walton Family Foundation develop its public safety funding strategy for these counties.The research reported here was conducted through the RAND Justice Policy Program, which spans both criminal and civil justice systems issues with such topics as public safety, effective policing, police-community relations, drug policy and enforcement, corrections policy, use of technology in law enforcement, tort reform, catastrophe and mass-injury compensation, court resourcing, and insurance regulation. Program research is supported by government agencies, foundations, and the private sector.RAND Justice, Infrastructure, and Environment (JIE) conducts research and analysis in civil and criminal justice, infrastructure development and financing, environmental policy, transportation planning and technology, immigration and border protection, public and occupa...

show abstract

The effectiveness of Multisystemic Therapy (MST): A meta-analysis

Cited by 238 publications

References 43 publications

Fitting three-level meta-analytic models in R: A step-by-step tutorial

Fitting three-level meta-analytic models in R: A step-by-step tutorial

Placement and Delinquency Outcomes Among System-Involved Youth Referred to Multisystemic Therapy: A Propensity Score Matching Analysis

Leveraging Research and Philanthropy to Reduce Crime and Violence in the Mississippi Delta

Contact Info

Product

Resources

About