在临床环境中系统地整合多告密者外部化评级
Madison Aitken1,2,3, André Plamondon4, John Krzeczkowski5
1Centre for Addiction and Mental Health, Toronto, Canada. madison.aitken@camh.ca.
Research on child and adolescent psychopathology
|October 3, 2023
概括
整合父母,教师和青少年对外部化问题的评价,使用简单的模型最好预测临床结果. 复杂的模型不能为儿童行为问题提供优越的预测有效性.
科学领域:
- 儿童和青少年心理学 儿童和青少年心理学
- 临床心理学 临床心理学
- 心理测量 心理测量 心理测量
背景情况:
- 对外化问题的临床评估 (过度活动-注意力不集中,行为问题,对立性-蔑视) 通常需要来自多个来源的投入:父母,青年和教师.
- 整合这些多信息者评级对于准确的诊断和治疗规划至关重要.
- 现有的方法从简单的 (例如,最高评级) 到复杂的统计模型.
研究的目的:
- 评估不同方法的预测有效性,以整合多信息,外部化问题的维度等级.
- 在预测临床结果时,将简单的评分程序与复杂的潜变量模型进行比较.
- 确定儿童的年龄和性别是否会抑制这些整合方法的预测准确性.
主要方法:
- 利用了来自2264名临床转诊的年轻人 (6-18岁) 以及他们的父母,老师和自我报告的数据.
- 评估了简单整合方法 (最高告知者,所有告知者) 和复杂模型 (双因素,调节的非线性因子分析) 的预测有效性.
- 预测结果包括临床损伤,并发症和临床遭遇数量.
主要成果:
- 一个简单的模型包含了所有信息提供者的评级,在所有评估结果中显示出最强的预测有效性.
- 这种简单的方法的性能与仅使用最高的告密者评级或更复杂的潜变量模型相比或更好.
- 将儿童的年龄和性别作为调节器并没有提高因子模型的预测能力.
结论:
- 每个信息者 (家长,老师,青少年) 提供有价值的,独特的信息,用于预测外化障碍的临床相关结果.
- 建议采用简单的模型,保留所有信息提供者的信息,而不是复杂的潜在变量方法来整合多源数据.
- 目前的证据不支持使用复杂的统计模型而不是更简单的方法来评估外部化问题.
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