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A comparison of measures to screen for social, emotional, and behavioral risk.

Faith G MillerDaniel CohenSandra M ChafouleasT Chris Riley-TillmanMegan E WelshGregory A Fabiano
Published in: School psychology quarterly : the official journal of the Division of School Psychology, American Psychological Association (2014)
The purpose of this study was to examine the relation between teacher-implemented screening measures used to identify social, emotional, and behavioral risk. To this end, 5 screening options were evaluated: (a) Direct Behavior Rating - Single Item Scales (DBR-SIS), (b) Social Skills Improvement System - Performance Screening Guide (SSiS), (c) Behavioral and Emotional Screening System - Teacher Form (BESS), (d) Office discipline referrals (ODRs), and (e) School nomination methods. The sample included 1974 students who were assessed tri-annually by their teachers (52% female, 93% non-Hispanic, 81% white). Findings indicated that teacher ratings using standardized rating measures (DBR-SIS, BESS, and SSiS) resulted in a larger proportion of students identified at-risk than ODRs or school nomination methods. Further, risk identification varied by screening option, such that a large percentage of students were inconsistently identified depending on the measure used. Results further indicated weak to strong correlations between screening options. The relation between broad behavioral indicators and mental health screening was also explored by examining classification accuracy indices. Teacher ratings using DBR-SIS and SSiS correctly identified between 81% and 91% of the sample as at-risk using the BESS as a criterion. As less conservative measures of risk, DBR-SIS and SSiS identified more students as at-risk relative to other options. Results highlight the importance of considering the aims of the assessment when selecting broad screening measures to identify students in need of additional support.
Keyphrases
  • mental health
  • high school
  • healthcare
  • physical activity
  • machine learning
  • high throughput
  • deep learning
  • african american
  • psychometric properties