用于筛查自闭症谱系障碍的学龄儿童行为清单分量表的制定和验证。
The Development and Validation of a Subscale for the School-Age Child Behavior CheckList to Screen for Autism Spectrum Disorder.
机构信息
UvA minds: Academic Treatment Center for Parents and Children, Banstraat 29, 1071 JW, Amsterdam, the Netherlands.
Research Institute of Child Development and Education, University of Amsterdam, Nieuwe Achtergracht 127, 1018 WS, Amsterdam, the Netherlands.
出版信息
J Autism Dev Disord. 2023 Mar;53(3):1034-1052. doi: 10.1007/s10803-022-05465-7. Epub 2022 Feb 15.
The first aim of this study was to construct/validate a subscale-with cut-offs considering gender/age differences-for the school-age Child Behavior CheckList (CBCL) to screen for Autism Spectrum Disorder (ASD) applying both data-driven (N = 1666) and clinician-expert (N = 15) approaches. Further, we compared these to previously established CBCL ASD profiles/subscales and DSM-oriented subscales. The second aim was to cross-validate results in two truly independent samples (N = 2445 and 886). Despite relatively low discriminative power of all subscales in the cross-validation samples, results indicated that the data-driven subscale had the best potential to screen for ASD and a similar screening potential as the DSM-oriented subscales. Given beneficial implications for pediatric/clinical practice, we encourage colleagues to continue the validation of this CBCL ASD subscale.
本研究的首要目标是构建/验证一个基于数据驱动（N=1666）和临床专家（N=15）方法，并考虑到性别/年龄差异的学龄期儿童行为检查表（CBCL）亚量表，以筛查自闭症谱系障碍（ASD）。此外，我们将这些与先前建立的 CBCL ASD 特征/亚量表和基于 DSM 的亚量表进行了比较。第二个目标是在两个真正独立的样本（N=2445 和 886）中进行交叉验证。尽管所有亚量表在交叉验证样本中的区分能力相对较低，但结果表明，基于数据驱动的亚量表在筛查 ASD 方面具有最佳潜力，与基于 DSM 的亚量表具有相似的筛查潜力。鉴于对儿科/临床实践的有益影响，我们鼓励同事们继续验证这个 CBCL ASD 亚量表。
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