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Dual factor mental health model: Validation through mixture modeling and cut scores

机译：双因素心理健康模型：通过混合建模和切割分数验证

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The dual-factor model (DFM) of mental health affords educators an expanded view of student social-emotional and behavioral functioning and may help identify students in need of school-based mental health services who would otherwise go unnoticed with traditional screening methods. With a focus on integrating subjective well-being into the conceptualization of mental health, the DFM may be one paradigm to aid in supporting students. However, without greater clarity regarding accurate and feasible methods of measuring student functioning and grouping behaviors into the DFM, schools may not be able to utilize this strategy within broader multi-tiered systems of support. As such, this study explores the stability of the DFM categories over the course of one school year and compares student grouping using theoretically-informed cut-score analysis and empirically informed mixture modeling. Results indicate that data may be best represented by different types of DFM latent groups over the course of one school year and may have little congruence with cut-score methods of categorization. Implications for the utility of the DFM for providing a comprehensive picture of school-wide mental health are discussed.
机译：心理健康的双因素模型（DFM）为教育工作者提供了学生社会情绪和行为功能的扩展视图，并可能有助于确定需要学校心理健康服务的学生，否则传统筛查方法会忽略这些学生。DFM专注于将主观幸福感融入心理健康的概念化，它可能是一种帮助支持学生的范式。然而，如果没有更明确的测量学生功能和将行为分组到DFM中的准确可行方法，学校可能无法在更广泛的多层支持系统中使用这一策略。因此，本研究探索了DFM类别在一个学年内的稳定性，并使用理论上知情的切割分数分析和经验上知情的混合建模对学生分组进行了比较。结果表明，在一个学年内，不同类型的DFM潜在群体可能最能代表数据，并且可能与分类的切割分数方法几乎不一致。本文还讨论了DFM在全面了解学校心理健康方面的作用。

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