Speaker
Description
Author: Nathaniel von der Embse (University of South Florida)
Background: Youth mental health needs have increased over the last five years, and schools are an important context to facilitate preventative services (DeFrance et al., 2022). For prevention initiatives to be effective, proactive rather than reactive assessment is necessary. Research has supported the practice of universal screening to facilitate early identification and preventative mental health services in schools (Splett et al., 2018). However, relying on a single rater of mental health needs, such as the classroom teacher, often leads to under identification of students with internalizing mental health needs. This presentation will describe the newly developed Multi-Informant Decisional Assessment System (MIDAS), an assessment system that allows for the integration and use of multiple informants and multiple data sources for accurate and efficient identification of social, emotional, and behavioral risk.
Methods: This study employed a Bayesian statistical model to incorporate students’ background information to generate estimates of academic risk, used background information to generate cut scores in a training sample and validate them in a test sample, and identified the unique value of adding teacher and student self-reports with regard to sensitivity and specificity.
Results: This study included 6,634 teacher-student dyads from 45 school districts, each with a rating on a universal mental health screening assessment. Demographic and academic performance data were included for each dyad. Results demonstrated the promise of incorporating background information in the accurate identification of students with low, medium, and high risk for mental health needs.
Discussion: Results demonstrate the promise of a novel data aggregation system of incorporating multiple raters on a proactive indicator of mental health needs that may ultimately lead to more accurate and timely delivery of preventative mental health services in schools. Prevention initiatives should incorporate multiple informants, and novel aggregation tools.
| Conflict of interest | None |
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