The Use Of Student Self Report Screening Data For Mental Health Risk Surveillance

Child and adolescent mental health disorders are known to increase the risk for numerous poor school and life outcomes for children and adolescents including suicidal ideation and attempts, academic underachievement and school dropout, substance use and disorders, and physical fighting or victimization by a weapon (Bradley, Doolittle, & Bartolotta, 2008; Brown & Grumet, 2000; Dowdy, Furlong, & Sharkey, 2012; O'Connell, Boat, & Warner, 2009). A preventive approach to mitigating associated impairment, morbidity, and poor outcomes in school settings has been advised for at least four decades (Cowen et al., 1973). The widespread adoption of preventive models, methods, and procedures for achieving this goal, however, has remained nascent in U.S. schools (Jamieson & Romer, 2005). Schools have long been identified as the community context of choice for delivering preventive mental health services. As major societal institutions, schools provide an organizational structure that reaches more children with more continuity than primary care, or any other child and family service setting (Doll & Cummings, 2008). Schools, however, are rather unprepared to provide preventive mental health services due to limited staff training, time commitment to educational service delivery, and a lack of assessment methods for delivering services such as universal screening (Fox, Halpern, & Forsyth, 2008; Levitt, Saka, Romanielli, & Hoagwood, 2007; O'Connell et al., 2009). Universal screening is the first step in any preventive, secondary prevention, or early intervention program for mental health problems (Levitt et al., 2007). A National Academies of Sciences report identified four levels of prevention, including: (1) universal prevention where community risk factors, such as school safety, are of interest, (2) selective prevention where high risk groups, such as children exposed to maternal depression, are identified for services, (3) indicated prevention where screening for behavioral and sub-syndromal symptoms is used to identify children for early intervention services [defined as behavioral or emotional risk (BER), for the purposes of this study], and (4) assessment for detection, diagnosis, and treatment of a mental health disorders (O'Connell et al., 2009). A central impediment to the adoption of universal screening measures for school-based screening of large groups of children has been the practicality of such measures, especially the associated personnel costs and test administration time that competes directly with the demand for academic instructional time (Dowdy, Ritchey, & Kamphaus, 2010). Although newer screening measures such as the one used in this study require only a few minutes per child, the practicality of screening thousands of students in numerous schools is yet to be determined (Dever, Raines, & Barclay, 2012). The current investigation sought to determine: (1) Whether or not a brief self-report screener of behavioral and emotional risk (BER) could be used universally in middle and high school with little concern about interference with instructional time or other practical concerns. (2) If the screener would produce score differences between schools that were consistent with school administrator concerns, which predicted that some schools were characterized by more adolescent BER than others. (3) Whether or not demographic variables such as child race/ethnicity, gender, SES, or grade level were strongly associated with screener scores. (4) If individual screener results demonstrated discriminant validity by assessing their association with classification as eligible for special education programs due to the presence of severe behavioral and emotional problems or diagnosed mental health disorders. Data were collected from 3 middle and 4 high schools in a mid-sized city in the Southeastern United States. A brief screening measure, the BESS Student Form, was administered to all students in groups, usually in homerooms, by school district employed school psychologists and school psychology doctoral students. Descriptive statistics for the sample by school are shown in Table 1. In order to test whether the screener would produce score differences between schools that were consistent with school administrator concerns, an Analysis of Variance (ANOVA) comparing schools was conducted. Socioeconomic status produced the most non-significant findings in that free or reduced lunch eligibility status, unlike the other demographic variables, did not produce any statistically significant differences between the BESS factors. In relationship to the fourth research question, special education status was linked statistically to only two of the BESS factors: adjustment (F = 60.10, p