Readiness for Positive Behavioral Interventions and Supports and School Mental Health Interconnection: Preliminary Development of a Stakeholder Survey

SMH

Between 12% and 32% of youth contend with serious emotional/behavioral problems (Burnett-Zeigler & Lyons, 2012; Merikangas et al., 2010). Less than one third of youth receive mental health services (Gaskin, Kouzis, & Richard, 2008; National Research Council, 2006), and if they do receive them, they are likely to receive them in schools (Rones & Hoagwood, 2000), the most universal natural environment for children apart from their homes. Socioemotional skills in children are unique predictors of academic performance and share a reciprocal relationship with academic performance (Guzman et al., 2011; Nadeem, Maslak, Chacko, & Hoagwood, 2010). Students’ mental health has been associated with increases in prosocial behavior and family engagement in school activities, as well as decreases in discipline referrals, special education referrals, and behavior problems (Stephan, Weist, Kataoka, Adelsheim, & Mills, 2007). Interventions promoting socioemotional learning are linked with improved attitudes and school functioning and decreases in depression (Merrell, 2010).

In reaction to this increased recognition of the links between mental health and student learning, as well as the realization that students’ mental health needs are largely met within the school setting (Eiraldi, Wolk, Locke, & Beidas, 2015), expanded SMH services have been developing around the nation (Evans, Weist, & Serpell, 2007; Weist, Evans, & Lever, 2003). These programs emphasize school–community system partnerships to implement a full continuum of promotion, prevention, early intervention, and treatment for youth in schools. Growth of SMH has been promoted through federal investments for national centers (e.g., University of Maryland, Center for School Mental Health) focused on advancing this field, and by evidence of improved access, reduced burden of care, and improved behavioral and school outcomes when these programs are done well (Adelman et al., 1999; Weist et al., 2007).

SMH services are delivered by a variety of professionals, including school psychologists, counselors, social workers, and community mental health staff, as well as others with backgrounds in clinical child psychology (Nabors, Weist, Tashman, & Meyers, 1999; Weist et al., 2009). Bringing community mental health staff into schools to augment the work of school-employed mental health staff increases the ability of schools to respond to student mental health needs. In addition, this would address the problem of too few school-employed staff and the fact that many of them contend with position constraints, such as psychologists in roles that emphasize evaluation (Weist, Lever, Bradshaw, & Owens, 2014). However, SMH services typically lack an implementation structure and, as a result, are implemented with high variability within schools and school districts, with many different arrangements, and often are not connected to PBIS. Failing to connect to PBIS also compromises SMH in relation to missed opportunities for effective teamwork and data-based decision making, two core features of PBIS (Barrett et al., 2013).

It is also imperative that professionals in the school and community receive adequate and ongoing training in mental health promotion and SMH service delivery (Weist, 2005). Massey, Armstrong, Boroughs, Henson, and McCash (2005) recommended that teachers receive ongoing training to increase understanding of the intervention process and how interventions function with the academic curriculum. This training also promotes intervention fidelity and consistency.

Mellin and Weist (2011) suggested conceptualizing collaboration among SMH professionals using a social capital framework. By viewing these interconnected relationships from a perspective of mutual support and trust, SMH services and outcomes for students can be enhanced by sharing resources and information. For instance, mental health practitioners in the community can learn about the unique logistical, cultural, and legal issues regarding delivery of mental health services in the schools. Thus, increasing knowledge in this organic fashion can increase the effectiveness of interventions and streamline SMH service delivery.

Toward the Interconnection of PBIS and SMH

Interconnecting SMH with PBIS is a logical next step for adding depth and quality to PBIS and improving team functioning and data-based decision making for SMH. More than 18,000 schools across the U.S. use the PBIS framework and are familiar with its operation (Barrett et al., 2013). Although national data are less clear for SMH programs, at least 5,000 schools have formal connections with community mental health services in the national survey of SMH conducted by the Substance Abuse and Mental Health Services Administration (Foster et al., 2005). Thus, interconnection of PBIS and SMH has the potential to enhance quality of programming in many U.S. schools and create synergies and economies of scale in a collaborative approach that capitalizes on the respective strengths of each approach (Barrett et al., 2013).

Another factor integral to successful PBIS–SMH interconnection is buy-in and support of school administrators, teachers, and key stakeholders. “Buy-in among school professionals” was one of the top five essential factors impacting SMH collaboration (Mellin & Weist, 2011). According to Langley, Nadeem, Katoaka, Stein, and Jaycox (2010), lack of teacher buy-in was a major barrier for implementing SMH services. Buy-in from administrators tends to diffuse toward teachers and other staff (Massey et al., 2005).

Data suggest that PBIS and SMH initiatives are effective in ameliorating behavioral issues and promoting academic success. Bradshaw et al.’s (2010) examination of Tier I PBIS indicated that over 5 years in 37 Maryland public schools, both the number of student discipline referrals and suspensions decreased significantly. Check-In/Check-Out (Tier II) resulted in significant decreases in student discipline referrals and teacher ratings of student problem behaviors (McIntosh et al., 2009), as well as decreases in the frequency and severity of problem behaviors (Todd et al., 2008). Positive effects of standardized test scores and reductions in office discipline referrals and suspensions were also observed (Lassen et al., 2006). Similarly, promotion of SMH has been linked to increases in school performance. By improving students’ overall well-being, other peripheral concerns that are detrimental to academic achievement (e.g., discipline referrals, truancy, drop outs, and lack of school engagement) are ameliorated (Stephan et al., 2007).

Unfortunately, bringing PBIS and SMH together can be difficult for school district personnel. Many schools and districts, even those presently using PBIS in any capacity, may be unprepared to integrate SMH services into their current programming. A central barrier to PBIS–SMH interconnection is the lack of a measure to evaluate the readiness of schools and districts for undertaking this process. Evaluating readiness is the first part of PBIS–SMH interconnection, which is a multistep process. Without such a measure, program implementers and school leaders do not have a formal method by which to gauge the level of preparedness for PBIS–SMH interconnection. When stakeholders perceive an intervention as unnecessary, too expensive, or incompatible with their values and beliefs, the intervention is likely to fail (Fixsen, Naoom, Blase, Friedman, & Wallace, 2005). Thus, evaluating readiness for intervention implementation allows interventionists to identify sites in which stakeholders are likely to endorse the plan.

Prior to interconnecting PBIS and SMH, it is imperative to consider issues surrounding readiness to adopt change. This is an especially critical step, as PBIS–SMH interconnection is likely an unfamiliar concept. Readiness for integrating PBIS and SMH can be conceptualized using the science of implementation framework by Fixsen and colleagues (2005). This framework includes the steps of exploration and adoption, program installation, initial implementation, full operation, innovation, and sustainability. The first phase of this framework is exploration and adoption, in which a program is investigated to determine its goodness of fit with current issues, needs and resources of the community, and needs regarding evidence-based practice and programming. Based on the information gathered during exploration, a choice is made regarding whether to adopt and implement the intervention or continue use of current programming. If the intervention will be adopted, a plan for implementation is developed, with ideas for facilitating operations and reducing any barriers that would hinder implementation. Readiness is a critically important construct on whether a school or program moves through initial steps of embracing and beginning to implement an innovation as reviewed here.

The literature is somewhat limited regarding readiness to implement PBIS and SMH. In a qualitative study, Savage, Lewis, and Colless (2011) found that school readiness for school-wide PBIS implementation is necessary prior to adopting the intervention, as well as after for sustainability purposes. Initial implementation was also facilitated due to the involvement of all school personnel, from administrators to teachers to support and custodial staff. The authors also indicated that readiness consists of perceiving a need for change, being open to acquiring new skills, and having sufficient preparation to implement the intervention. Tools to promote a systematic readiness process are now available on various websites connected to PBIS implementation, including www.pbis.org, www.istac.net, and http://flpbs.fmhi.usf.edu.

Handler et al. (2007) noted several systemic issues germane to readiness for PBIS implementation. For instance, a leadership team consisting of school and community stakeholders should be in place to guide adoption and implementation. Consistent communication between administrators and school staff can encourage adoption and early implementation. In addition, support from the school district is imperative. Specifically, the district must realize that systemic change requires some time, but the benefits of prevention and early intervention will reveal themselves later with improved academic performance and graduation rates and fewer discipline referrals and behavioral problems (Sugai & Horner, 2008).

The Interconnected Systems Framework (ISF) highlights SMH service delivery within a multi-tier PBIS framework (Eber, Weist, & Barrett, 2013). By combining the two systems, the ISF can ameliorate issues with each system (e.g., inconsistent implementation of Tiers II and III in PBIS, lack of data-based decision making in SMH services). Key features of the ISF include utilizing existing education and mental health systems, EBIs, and collaboration among professionals and stakeholders at the state, district, community, and school levels. Furthermore, fidelity and efficacy are assessed regularly, with changes implemented as needed to improve quality of service delivery and outcomes.

According to Paternite and Butts (2013), the ISF is operating in 16 states through each state’s Community of Practice (CoP) in School Behavioral Health, in collaboration with the IDEA Partnership. Paternite and Butts discussed examples from Pennsylvania, Utah, and Montana. These states deliver comprehensive SMH services within the PBIS framework that are child-centered, evidence-based, and data-driven. There is also emphasis on multidisciplinary collaboration and feedback from community stakeholders, families, and youth.

With this limited literature base on readiness for PBIS and SMH implementation, measures for PBIS–SMH readiness are even scarcer. There is also no measure to evaluate readiness to integrate PBIS and SMH, despite calls for development of such a measure (Kincaid, Childs, Blase, & Wallace, 2007). Some measures assess similar constructs such as practitioners’ perceptions of PBIS implementation and attitudes regarding PBIS facilitators and barriers (see Bambara, Nonnemacher, & Kern, 2009; Michaels, Brown, & Mirabella, 2005; Lewis-Palmer, Horner, Todd, & Sugai, 2001), but none directly pinpoint readiness for PBIS–SMH interconnection. Thus, existing measures appear to focus on implementation and fidelity, barriers and facilitators to implementation, and service utilization. The current research aims to remedy this with the preliminary development of a readiness for PBIS–SMH interconnection measure.

Methods

This study involved a mixed method design of qualitative analyses (survey of relevant stakeholders, consultation with key informants) and quantitative analyses (survey development, psychometric analyses) to develop a survey for readiness to integrate PBIS and SMH. Therefore, the study and subsequent analyses are exploratory in nature.

This study was carried out in four phases. The first phase was a pilot study, conducted in September of 2011. A sample of key stakeholders with interests in PBIS and SMH were asked to complete an open-ended survey to determine fundamental factors for satisfactory implementation and interconnection of PBIS and SMH. Following the pilot study, Phase II involved aggregating and qualitatively analyzing these data to develop common themes relevant to PBIS–SMH interconnection. In addition, the principal investigator (PI) consulted with experts in PBIS, SMH, and related fields and reviewed the applicable literature. This information was used to develop a 35-item readiness for PBIS–SMH interconnection survey.

In Phase III of the study, the 35-item survey was revised further through additional literature review and consultation with experts in PBIS and SMH and survey construction. Based on these consultations, survey items were revised, added, or discarded accordingly. New items were added to capture constructs identified through the literature review and consultations, resulting in a 98-item survey. Lastly, Phase IV included converting the survey to an online format, disseminating the survey to potential participants via email, and collecting and analyzing the data. All four phases of the study were reviewed and approved by the Institutional Review Board of the sponsoring university. Each phase of the study is discussed further below.

Participants

Participants included 346 individuals from a national sample. The National CoP for School Behavioral Health played an integral role in guiding and promoting interest in the survey. The major survey was discussed on several of their regularly scheduled conference calls. Furthermore, the survey and corresponding link were advertised through postings on National CoP listserv announcements, as well as websites of affiliated organizations. The target population for the study was school and community stakeholders, teachers, administrators, family members, mental health practitioners, and other professionals working with PBIS and SMH. Inclusion criteria for participants were individuals who are currently working in a setting using PBIS and SMH (e.g., a school or school district), delivering mental health services to youth enrolled in school, and/or community members who support PBIS and SMH services.

Of the 346 participants, 273 (78%) completed the major survey. This sample more than satisfies recommendations from Everitt (1975), Kline (2010), and Loehlin (2004) to ensure valid factor analyses. The following demographics describe those who completed the survey in its entirety (also refer to Table 1). Most participants were female (n = 214, 78.4%). The majority identified as school social workers (n = 56, 20.5%). Other highly represented professions included general education teachers, school psychologists, and school administrators. Participants also worked at the state, district, and building levels, with most indicating the latter (n = 172). (Refer to Table 2 for further information.) Practitioners with 25 or more years experience in their field comprised 20.9% of the sample. In terms of school level, 38.5% of participants worked in elementary (K–5) schools. Most participants worked in non-metropolitan urban settings, defined as areas having more than 2,500 but less than 250,000 residents (50.9%). Also, 15.4% of participants worked in settings where 41% to 50% of students received free or reduced lunch.

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Table 1.

Major Survey Participant Demographics.

Variable	n	Percentage
Gender
Female	214	78.4
Male	59	21.6
Profession
Clinical/counseling/community psychologist	11	4.0
Clinical social worker	18	6.6
Faculty/researcher	10	3.7
Family member/advocate	4	1.5
Government official	4	1.5
Nurse	1	0.4
Related service provider (speech therapy, occupational therapy)	13	4.8
School administrator	26	9.5
School counselor	8	2.9
School psychologist	27	9.9
School social worker	54	19.8
Teacher (regular education)	39	14.3
Teacher (special education)	15	5.5
Other	43	15.8
Level currently working (may check more than one)
State level	35	12.8
District level	115	42.1
Building level	172	63.0
Years of experience in field
1–5 years	45	16.5
6–10 years	45	16.5
11–15 years	43	15.8
16–20 years	47	17.2
21–25 years	36	13.2
More than 25 years	57	20.9
School level
Preschool	3	1.1
Elementary (Grades K–5)	105	38.5
Elementary/middle (K–8)	39	14.3
Middle (Grades 6–8)	37	13.6
Middle/high (6–12)	17	6.2
High (Grades 9–12)	35	12.8
Alternative school	8	2.9
Other	29	10.6

Note. N = 273.

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Table 2.

Demographics by Profession and State/District/Building Level.

Profession	n	State	District	Building
Clinical/counseling/community psychologist	11	1	5	8
Clinical social worker	18	1	5	13
Faculty/researcher	10	6	2	2
Family member/advocate	4	2	3	1
Government official	4	3	2	1
Nurse	1	0	1	0
Related service provider (speech therapy, occupational therapy)	13	1	6	10
School administrator	26	2	14	15
School counselor	8	1	2	5
School psychologist	27	2	18	17
School social worker	54	3	19	38
Teacher (regular education)	39	0	13	28
Teacher (special education)	15	2	5	13
Other	43	11	20	21

Note. N = 273.

Results

According to descriptive analyses, all responses were normally distributed. Skew and kurtosis index scores were within normal limits. Item means ranged from 2.01 (SD = 0.72) to 3.16 (SD = 0.76). Spearman’s rho correlations ranged from –.19 to .89. Cronbach’s α of .98 indicated a high level of internal consistency. A priori between-group analyses were planned to evaluate differences in responding by profession; however, due to the small n per each group, there was insufficient power to detect meaningful group differences (Cohen, 1988).

Following these analyses, a confirmatory factor analysis (CFA) was conducted to ascertain if the survey items load onto the following factors: support/buy-in/resources, collaboration and teamwork, positive student outcomes, and use, understanding, and applications of PBIS. These factors were selected based on the pilot survey data and subsequent key themes, as well as Bambara et al. (2009) and Handler et al. (2007). Factor analyses were carried out using MPlus software (Muthén & Muthén, 1998–2011), whereas all other analyses were calculated with SPSS software (IBM, 2011). The CFA was conducted using the specified model and the weighted least squares parameter estimation (WLSMV), with various model fit estimates examined. The root mean square error of approximation (RMSEA) estimate was .082. According to Hu and Bentler (1999) and MacCallum, Browne, and Sugawara (1996), an RMSEA estimate of .05 is indicative of good model fit, whereas .08 suggests a “mediocre” model fit. The comparative fit index (CFI) of .83 was below the recommended cutoff of .95 (Brown, 2006; Hu & Bentler, 1999). Although the CFI for this sample approached the cutoff, this suggested that the hypothesized model is not an optimal representation of the data. Similarly, the Tucker–Lewis index (TLI) of .83 was below the suggested cutoff of 1.00 (Brown, 2006). Based on the current sample, these estimates did not support the hypothesized factor structure as a strong model.

Thus, an exploratory factor analysis (EFA) with WLSMV was conducted to develop a factor structure from the data. WLSMV is robust to variation in model complexity, sample size, and non-normality (Brown, 2006; Muthén & Muthén, 1998–2011). To promote a theoretically strong factor structure, the number of factors was limited to four. Eigenvalues from the EFA were examined using the scree test. In Figure 1, the line drops sharply at one, indicating a one-factor solution for the survey. Because the data suggested a one-factor solution as opposed to the originally hypothesized four-factor structure, readiness to interconnect PBIS and SMH may be a unitary construct. This is examined further in the Discussion section.

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Figure 1.

Scree plot for exploratory factor analysis.

The EFA data were evaluated further to determine which items should be removed from the survey to promote survey use and utility. Eigenvalues for each item under the single-factor structure ranged from .436 to .895. Recommendations for eigenvalue cutpoints vary; Kline (2010) suggested .50, whereas Sterba (2011) reported using eigenvalues of .70 or greater for high indicator loadings, per Nunnally and Bernstein’s (1994) recommendation. For this study, a rigorous cutoff score of .70 was used. Thus, 62 items of the original 98 remained. The additional item “PBIS and SMH teams meet together” was also retained, as communication between these two groups is essential to PBIS–SMH interconnection. Thus, the final survey has 63 items, including 34 major items with some items having sub-responses. Some items were slightly reworded through consultation with PBIS–SMH experts to increase clarity. An α of .98 suggested excellent internal consistency, and was the same α calculated for the original survey.

Discussion

Although pairing PBIS and SMH is advantageous to students, schools, families, and communities, it can be a large undertaking. To ensure that PBIS–SMH interconnection is productive and useful to the school and community, it is imperative to evaluate readiness prior to installation and implementation (Fixsen et al., 2005). To support the successful adoption and installation of the intervention, stakeholders and school staff must view the intervention as a potential solution to problems identified within the school community. If they are not prepared to take action to address these problems, the intervention has a small chance of success.

The interconnection of PBIS and SMH is an essential next step in promoting improvements in children’s behavior and functioning across domains. As outlined by Eber et al. (2013) in the ISF, SMH services can be integrated within the multi-tiered framework of PBIS, allowing parents, teachers, mental health professionals, and others to tailor the intervention to the student’s unique needs. Furthermore, PBIS is an evidence-based framework (see Sugai, Horner, et al., 2000; Sugai, Sprague, et al., 2000), and is featured on the National Registry of Evidence-Based Programs and Practices (NREPP) of the Substance Abuse and Mental Health Services Administration (SAMHSA). Similarly, there are many evidence-based mental health interventions and prevention strategies available to meet a variety of needs (Browne, Gafni, Roberts, Byrne, & Majumdar, 2004; Splett & Maras, 2011), as well as studies examining strategies to overcome barriers to implementation (Langley et al., 2010; Reinke, Stormont, Herman, Puri, & Goel, 2011; Schaeffer et al., 2005). In addition to the variety of benefits of the ISF, this is a practical way to increase the availability of mental health services in the schools. PBIS and SMH fit together to promote better mental health, as well as academic and socioemotional competence.

The development of an initial survey to assess the readiness for PBIS–SMH interconnection survey is a timely addition to the field and the literature. This survey and the study of its psychometric properties can provide interventionists and researchers with a starting point for gauging preparedness of schools and communities to integrate PBIS and SMH. Prior to the current study, no such measure existed despite a critical need for this tool discussed in the literature to move this line of research and intervention forward (Kincaid et al., 2007).

Through a pilot study and consultation with experts in the fields of PBIS, SMH, and survey construction, the original 98-item survey was developed. A CFA indicated that the hypothesized four-factor structure (support/buy-in/resources, collaboration and teamwork, positive student outcomes, and use, understanding, and applications of PBIS) was not an optimal representation of readiness. An EFA revealed a single-factor structure, with item eigenvalues ranging from .436 to .895. Therefore, readiness for PBIS–SMH interconnection appears to be a unitary construct. To facilitate interpretability of results, items with eigenvalues below .70 were removed. The survey was revised through consultation with PBIS–SMH experts, resulting in a 63-item survey with 34 major questions and some sub-items for selected questions.

The single-factor structure has several important theoretical implications. The hypothesized four-factor structure was not supported by the data, despite research highlighting these factors as integral players in intervention adoption and implementation (Fixsen et al., 2005; Handler et al., 2007; Nastasi & Hitchcock, 2009). From a practical applications perspective, key elements must be in place to successfully adopt and implement any intervention (Fixsen, et al., 2005). However, the current study does not support discrete sub-factors of the overarching PBIS–SMH readiness construct, although several areas contribute to the single construct. Nevertheless, it is imperative that interventionists examine various areas for adequate buy-in and resources prior to adoption and installation. For example, a lack of teaming structures may undermine the success or, at the very least, attenuate the intervention’s success.

In addition to raising awareness of readiness issues in general, the Readiness for PBIS–SMH Interconnection Survey has utility on several fronts. First, being available online at no cost (see Anello & Weist, 2013) removes the barriers of accessibility that often influence intervention adoption and implementation in the schools. Because assessment and intervention are iterative processes, the survey can be used at multiple time points to evaluate readiness as schools and communities move toward full implementation of PBIS–SMH interconnection. Also, communities and school districts may use the survey to identify pilot schools for PBIS–SMH interconnection that may later serve as exemplars of the intervention.

Furthermore, the survey can guide school and community leaders as they invest in building PBIS–SMH interconnection. Because implementation of PBIS (Spaulding et al., 2008) and SMH (Splett & Maras, 2011) can vary so greatly, an examination of resources and fidelity can assist schools in carrying out PBIS and SMH interventions as designed by their developers. From that point, schools may evaluate their SMH delivery, available services, and partnerships with service providers in the community. Discussing these issues can lead to opportunities for building buy-in and support among administrators, school staff, and community members. The power of these working relationships should not be underestimated. Social capital can be quite influential when building support for new initiatives (Mellin & Weist, 2011).

In addition to identifying implementation supports, the survey also points to several paradigm shifts necessary to move ISF research and practice forward. Because PBIS and SMH focus on student needs and supporting their academic and socioemotional growth, merging these systems can emphasize person-centered approaches to service and intervention planning. The introduction of cross-systems problem solving teams also brings a multidisciplinary approach to ameliorating issues identified by key stakeholders (Eber et al., 2013). By evaluating readiness and the needs of the students and the community, schools and districts can introduce new services to address these issues, as well as modifying current services and delivery modalities accordingly. Similarly, decisions for intervention selection and service delivery are data- and evidence-based, which can facilitate decision making and determining next steps.

The availability of readiness data about PBIS/SMH integration may be useful to community and school leaders as they work together to resolve the fragmentation of youth mental health services. Unfortunately, there is still a lack of coordination and communication among the various professionals working with school-age youth experiencing mental health issues (Kerns et al., 2014). Communication among service providers, regardless of modality, is in the best interest of the populations served. By allowing for more communication among these service providers, some of whom may be unaware that they are serving the same students, consistency in service provision can be increased, while the redundancy of some services may be reduced. According to Eber et al. (2013), a key feature of ISF implementation is increasing communication and coordination of services among service providers. By having professionals from various disciplines lead and serve on teams at multiple points of intervention, providers are able to collaborate and streamline services, which can lead to maximizing resources and delivering effective EBIs. Furthermore, systematic progress monitoring and fidelity checks are built into the ISF, which is also crucial for intervention effectiveness. This collaboration is especially timely, in light of mental health care reform and corresponding changes in health care legislation.

The survey also fits with recent research on classroom management interventions. These interventions point to the need for measurement of readiness to implement interventions, as well as ongoing measurement of integrity and fidelity of implementation. Farmer, Reinke, and Brooks (2014) discussed classroom management strategies within the framework of linking behavioral and ecological perspectives via social-interactional considerations. Supporting students with emotional and behavioral problems requires addressing these concerns within the contexts of classroom, school, and community. Furthermore, there are several domains of classroom functioning, including learning, classroom procedures, and social interactions, to consider when selecting an appropriate intervention. Thus, the readiness survey can be used to bridge research and practice for implementation of EBIs in classrooms, as well as in identifying needs according to the various contexts discussed by Farmer and colleagues.

Similarly, Kern et al. (2015) discussed implementation of the Center for Adolescent Research in Schools (CARS) intervention with secondary students experiencing emotional/behavioral problems. The authors used an iterative process and data-based decision making to tailor the intervention to students’ needs, as well as implementing the minimally sufficient level of intervention necessary. Data were collected on a variety of outcomes, including student factors such as academic achievement scores, as well as measures of life satisfaction, anxiety, and depression. Observational data of teachers’ classroom practices was also collected, in addition to office discipline referrals, suspensions, absences, and students’ use of community mental health services. Perceptions of intervention feasibility and acceptability were also assessed. The authors’ focus on linking school and community mental health services, in addition to providing the level and type of intervention necessary according to student and school needs, is key for promoting buy-in and integrity of implementation.

Motoca et al. (2014) studied the directed consultation model within a professional development context and its effects on teachers’ classroom management. In this consultee-focused intervention, sixth-grade teachers participated in the Supporting Early Adolescent Learning and Social Supports (SEALS) program, with the aim of fostering supportive classroom environments for students transitioning to middle school. By using structured classroom observations, the authors evaluated teachers’ classroom management practices pre- and post-intervention. As compared with control classrooms, teachers who participated in the intervention were rated higher on positive feedback to students, behavior management, classroom structure, communication with students, instructional protocols, communication, and the use of groups and social dynamics. By integrating an evidence-based classroom management intervention within an ecological framework, teachers were given targeted strategies to embed in their instruction.