调和不一致的普遍查数据以改善决策:贝叶斯逻辑回归方法.
Nathaniel von der Embse1, Sonja Winter2, Wes Bonifay2
1Department of Educational and Psychological Studies at the University of South Florida.
Journal of school psychology
|June 12, 2025
概括
整合学生背景数据可以提高心理健康查的准确性. 这种方法有助于更有效地识别需要早期干预服务的学生,而不是单一评价方法.
科学领域:
- 教育心理学教育心理学
- 儿童和青少年心理健康
- 教育中的数据科学教育中的数据科学
背景情况:
- 许多有心理健康需求的学生缺乏及时的支持.
- 普遍查是早期干预的关键,但目前的方法有限.
- 多信息者评估是最佳实践,但不适用于普遍查.
研究的目的:
- 开发一个贝叶斯模型,使用学生的背景数据进行普遍的心理健康查.
- 为了验证从背景信息中获得的切割分数.
- 评估教师和学生自我报告的附加值.
主要方法:
- 用贝叶斯统计模型将学生的背景信息 (人口统计,推,风险状况) 纳入.
- 背景信息在培训样本中产生了切割分数,并在测试样本中得到验证.
- 对敏感性和特异性进行了分析,使用和不使用教师/学生自我报告.
主要成果:
- 结合背景信息显著改善了对有心理健康需求风险的学生的准确识别.
- 该模型在将学生分为低风险,中风险和高风险组时表现出有希望.
- 背景数据提高了风险识别的准确性.
结论:
- 学生的背景信息对于准确的普遍心理健康查是有价值的.
- 这种数据驱动的方法支持及时识别和干预学生.
- 未来的研究和实践应该考虑整合全面的学生数据来支持心理健康.
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