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Social, Emotional and Behavioral Screening Profiles Among Students in a Large Urban School District

机译:大城市学区学生的社会、情感和行为筛查概况

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摘要

Social, emotional, and behavioral (SEB) screening frequently employs a variable-based approach wherein individual scale scores indicate risk. However, a person-centered approach wherein risk is indicated by profiles, or constellations of scores across all scales, could help schools prioritize students based on the pervasiveness of SEB needs and match students to appropriate interventions. This study used latent profile analysis (LPA) to identify profiles within two diverse student samples ( n = 16,270 in year one; n = 4019 in year two) based on teacher ratings on the Behavior Intervention Monitoring Assessment System, Second Edition (BIMAS-2). Results suggested four profiles including one profile with elevated risk across all scales, one profile with low behavioral risk and above average social functioning, one profile with borderline risk across all scales, and one profile with typical scores across most scales. Implications for linking universal screening to intervention are discussed. Impact and Implications: Many school districts employ universal screening to prevent and address wide-ranging student needs. Grouping students based on shared sets of needs has the potential to efficiently identify and prioritize students with pervasive risk in order to match them to comprehensive services. Results from this study capture the first attempt to identify student need profiles using BIMAS-2 scores. Future research should refine this process to derive more usable and consistent student profiles allowing direct links to comprehensive student services.
机译:社会、情感和行为 (SEB) 筛查通常采用基于变量的方法,其中个人量表分数表示风险。然而,一种以人为本的方法,其中风险由概况或所有量表的分数星座来指示,可以帮助学校根据 SEB 需求的普遍性确定学生的优先级,并将学生与适当的干预措施相匹配。本研究使用潜在概况分析(LPA)根据行为干预监测评估系统第二版(BIMAS-2)的教师评级,识别两个不同学生样本(第一年n = 16,270;第二年n = 4019)的概况。结果显示有四个特征,包括一个在所有量表上都具有较高风险的特征,一个特征在行为风险较低且社会功能高于平均水平,一个特征在所有量表上都具有临界风险,以及一个特征在大多数量表上具有典型分数。讨论了将普遍筛查与干预联系起来的意义。影响和影响:许多学区采用普遍筛查来预防和满足广泛的学生需求。根据共同的需求对学生进行分组,有可能有效地识别和优先考虑具有普遍风险的学生,以便将他们与综合服务相匹配。这项研究的结果首次尝试使用 BIMAS-2 分数识别学生需求概况。未来的研究应该完善这一过程,以获得更有用和一致的学生档案,从而直接链接到全面的学生服务。

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