Implementing Effective Educational Practices at Scales of Social Importance.

Implementing evidence-based practices is becoming both a goal and standard across medicine, psychology, and education. Initial successes, however, are now leading to questions about how successful demonstrations may be expanded to scales of social importance. In this paper, we review lessons learned about scaling up evidence-based practices gleaned from our experience implementing school-wide positive behavioral interventions and supports (PBIS) across more than 23,000 schools in the USA. We draw heavily from the work of Flay et al. (Prev Sci 6:151-175, 2005. doi: 10.1007/s11121-005-5553-y ) related to defining evidence-based practices, the significant contributions from the emerging "implementation science" movement (Fixsen et al. in Implementation research: a synthesis of the literature, University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Research Network (FMHI Publication #231), Tampa 2005), and guidance we have received from teachers, family members, students, and administrators who have adopted PBIS.

Strategies for integrating mental health into schools via a multitiered system of support.

To fully realize the potential of mental health supports in academic settings, it is essential to consider how to effectively integrate the mental health and education systems and their respective resources, staffing, and structures. Historically, school mental health services have not effectively spanned a full continuum of care from mental health promotion to treatment, and several implementation and service challenges have evolved. After an overview of these challenges, best practices and strategies for school and community partners are reviewed to systematically integrate mental health interventions within a school's multitiered system of student support.

School-wide PBIS: An Example of Applied Behavior Analysis Implemented at a Scale of Social Importance.

School-wide Positive Behavioral Interventions and Supports (PBIS) is an example of applied behavior analysis implemented at a scale of social importance. In this paper, PBIS is defined and the contributions of behavior analysis in shaping both the content and implementation of PBIS are reviewed. Specific lessons learned from implementation of PBIS over the past 20 years are summarized.

Regression models and effect size measures for single case designs.

A regression modelling approach for the analysis of single case designs (SCDs) is described in this paper. The approach presented addresses two key issues in the analysis of SCDs. The first issue is that of serial dependence among the observations in SCDs. The second issue is that of an effect size measure appropriate for SCDs. As with traditional between-subjects experimental designs, effect size measures are critical in assessing the impact of interventions in SCDs. Although effect size measures when there is level change without trend are straightforward to obtain and have been well studied, the situation is different when there are changes in both level and trend. An effect size measure that combines changes in levels and slopes and that is comparable to the d-type effect size measure obtained in between-subjects designs is presented. Finally, an inferential procedure for assessing the effect of the intervention based on the effect size measure is provided and illustrated.

A cluster randomized trial to evaluate external support for the implementation of positive behavioral interventions and supports by school personnel.

BACKGROUND: Urban schools lag behind non-urban schools in attending to the behavioral health needs of their students. This is especially evident with regard to the level of use of evidence-based interventions with school children. Increased used of evidence-based interventions in urban schools would contribute to reducing mental health services disparities in low-income communities. School-wide positive behavioral interventions and supports (SWPBIS) is a service delivery framework that can be used to deliver universal preventive interventions and evidence-based behavioral health treatments, such as group cognitive behavioral therapy. In this article, we describe our ongoing research on creating internal capacity for program implementation. We also examine the cost-effectiveness and resulting school climate when two different levels of external support are provided to personnel as they implement a two-tier SWPBIS program. METHODS/DESIGN: The study follows six K - 8 schools in the School District of Philadelphia randomly assigned to consultation support or consultation-plus-coaching support. Participants are: approximately 48 leadership team members, 180 school staff and 3,900 students in Tier 1, and 12 counselors, and 306 child participants in Tier 2. Children who meet inclusion criteria for Tier 2 will participate in group cognitive behavioral therapy for externalizing or anxiety disorders. The study has three phases, baseline/training, implementation, and sustainability. We will measure implementation outcomes, service outcomes, child outcomes, and cost. DISCUSSION: Findings from this study will provide evidence as to the appropriateness of school-wide prevention and treatment service delivery models for addressing services disparities in schools. The effectiveness and cost-effectiveness analyses of the two levels of training and consultation should help urban school districts and policymakers with the planning and deployment of cost-effective strategies for the implementation of evidence-based interventions for some of the most common behavioral health problems in school children. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01941069.

A generalized least squares regression approach for computing effect sizes in single-case research: application examples.

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice.