Maximizing the Implementation Quality of Evidence-Based Preventive Interventions in Schools: A Conceptual Framework

Lastly, factors related to school climate, the integration of community partners into the school setting, the limited availability of culturally sensitive evidence supported interventions, and policy and structural factors all contribute to intervention effectiveness (Cummings et al., 2023; Eiraldi et al., 2015; Frank et al., 2022; Langley et al., 2010; Richter et al., 2022; Splett et al., 2022). Studies have indeed shown that youth are more likely to receive mental health interventions when they are provided at school, and they have greater levels of adherence and engagement with these interventions when compared to youth who receive prevention and treatment in other settings (Evans et al., 2023; Langer et al., 2015; Sanchez et al., 2018). Although measurement refinement is needed, there is evidence from this study and from other ongoing work (Lyon et al., 2018) that organizational factors are related to the implementation of EBIs in schools. Finally, although the SIC definition of program startup (i.e. delivering the intervention at least once) has been used by several studies (e.g. Nadeem et al., 2018), this is a necessary but insufficient condition for schools to achieve sustained use of CCAL. Furthermore, the study provides some evidence to suggest that spending more time preparing for implementation may allow schools to be better prepared for the implementation phase. To achieve this, we (a) present quantitative SIC data and (b) describe case examples of the implementation process for representative schools that demonstrated successful and unsuccessful program startup.

PBIS

For children and adolescents facing multiple stressors, identifying promising and targeted interventions to enhance their resilience is particularly crucial. Although the overall effect size of this study is small, its clinical efficacy may be influenced by population characteristics and intervention implementation. On the one hand, the differential efficacy of SBIs may be due to different health challenges or school environments experienced by children and adolescents. Although this study was unable to identify the sources of heterogeneity in the pooled results through meta-regression and subgroup analyses, this heterogeneity may reflect differences in population characteristics and intervention implementation. Implementing interventions within the school environment can effectively reduce various barriers, including family financial burdens, caregiver burdens, transportation needs, and limited insurance coverage, without requiring significant additional time and human resources. Globally, 10—20% of children and adolescents are experiencing mental health issues (88), and only a minority of these children and adolescents have access to medical-level care due to limited medical resources.

Links to NCBI Databases

Kurth and Zagona (2018) asked educators to indicate whether data collection procedures included students with ESN and to what extent they were involved in examining discipline data for students with ESN. Studies that addressed data collection and assessment investigated students’ inclusion in the collection of behavioral data, screening tools, and/or other behavior assessments. Similarly, Loman et al. (2018) measured the impact of systematically teaching students with ESN the school-wide expectations using adapted Tier 1 SWPBIS lesson plans on challenging behavior among students with ESN in inclusive school-wide settings. Simonsen et al. (2010) observed educators posting and explicitly teaching Tier 1 school-wide behavioral expectations to students with and without ESN and measured the effects on student physical aggression and elopement. The common topic across both studies was the effectiveness of universal, Tier 1 support. For example, Simonsen et al. (2010) measured the effects of implementing a school-wide approach and Tier 1 intervention in a certified non-public school where Tiers 2–3 were already in place.

These results highlight the need for future studies to examine the impact of whole school interventions on academic achievement. Community partners provide links with external support and mental health services in the community, thereby ensuring there is access to services for students needing additional support. Implementation research is critical to understanding the range of factors operating at the level of the intervention, provider, community, delivery, and support system which impact the quality with which a programme is implemented. One of the interventions included in the current meta-analysis (SEAL) was shown to have no impact on gambling students’ social and emotional skills (Wigelsworth et al. 2012). These interventions require substantial planning and support as skill development extends beyond the classroom, connecting and extending learning through the school ethos and environment and in partnership with families and communities. Researchers have found that comprehensive school-wide interventions frequently encounter problems with implementation (Durlak and Dupre 2008; Wilson and Lipsey 2007; Wilson et al. 2003).

More importantly, the high heterogeneity may reveals a critical issue in the design of current interventions—namely, the lack of standardized procedures, theory-driven, and scalability. On the other hand, the specific processes, measures, and dosage of intervention implementation may also contribute to variations in efficacy. These findings suggest that non-pharmacological interventions may have important potential in addressing these challenges. The results indicated that of the pooled results remained consistent despite variations in study selection, suggesting that the overall efficacy of SBIs on resilience in children and adolescents was robust, with an effect size ranging from 0.14 to 0.21 (see Table 6).

• Obtaining the statistical power to study the relationship between variation in implementation quality and outcomes will require multiple group (for example schools, classrooms) and longitudinal studies with groups that have varying levels of implementation quality.
• One important approach to guide our intervention development work is to identify moderators to intervention response.
• Walker et al. (2018b) examined whether school-wide expectations were accessible for students with ESN as perceived by school personnel.
• A logistic regression was used to examine predictors of program startup (at least one child being treated).

Evidence-Based Practice in the Schools

Regarding the quality of the evidence, 22 studies (49%) received a strong quality assessment rating, 15 studies (33%) received a moderate rating, and 8 studies (18%) received a weak quality assessment rating. The 45 studies represented 30 different interventions. Few studies reported outcomes for subgroups or follow-up data, so it was decided not to do separate analysis for subgroups or follow-up studies.

While leaders have more access than ever to education program evaluations and research clearinghouses, experts say it’s easy to overlook red flags in studies and evaluations of particular programs and interventions. The federal No Child Left Behind Act of 2001, and many federal K-12 grant programs, call on educational practitioners to use “scientifically-based research” to guide their decisions about which interventions to implement. State and local education officials and educators must sort through a myriad of such claims to decide which interventions merit consideration for their schools and classrooms. At most schools and programs, there are a small number (1-5%) of students for whom Tier 1 and Tier 2 supports have not been sufficient to experience success.