Universal screening for social, emotional, and behavioral risk is an important method for identifying students in need of additional or targeted support (Eklund and Dowdy in School Mental Health 6:40–49, 2014). Research is needed to explore how potential bias may be implicated in universal screening. We investigated student demographics as predictors of being placed at risk via a teacher-report measure: the Social, Academic, and Emotional Behavior Risk Screener as reported by Kilgus et al. (in: Theodore J. Christ et al. (eds) Social, academic, and emotional behavior risk screener (SAEBRS), 2014). Results indicated student demographics, including sex, special education status, free/reduced price lunch status, and identification as a student of color, were statistically significant predictors across multiple SAEBRS risk placements. The predictive power of student demographics was meaningful when evaluated independently (i.e., when each characteristic was considered separately with each risk placement) as well as when evaluated relatively or dependently (i.e., when all characteristics were taken together as a set to predict each risk placement). We discuss findings in the context of implications for implementation of universal behavioral screening amidst potential bias and serving students with identified levels of social, emotional, and behavioral risk.

对社会、情感和行为风险进行普遍筛查是识别需要额外或有针对性支持的学生的重要方法（Eklund 和 Dowdy in School Mental Health 6:40–49, 2014）。需要进行研究来探索潜在偏见如何与普遍筛查有关。我们通过教师报告措施调查了学生人口统计数据，将其作为面临风险的预测因素：Kilgus 等人报告的社会、学术和情感行为风险筛查器。（参见：Theodore J. Christ 等人（编）社会、学术和情绪行为风险筛查 (SAEBRS)，2014 年）。结果表明，学生人口统计数据（包括性别、特殊教育状况、免费/减价午餐状况以及有色人种学生身份）是多个 SAEBRS 风险安置的统计显着预测因素。当独立评估（即，当每个特征与每个风险放置分开考虑时）以及相对或依赖评估时（即，当所有特征作为一个集合来预测每个风险放置时），学生人口统计的预测能力是有意义的）。我们讨论研究结果对在潜在偏见中实施普遍行为筛查的影响，并为学生提供已确定的社会、情感和行为风险水平。