早期社会互动与学龄儿童的行为问题:来自理论和数据驱动方法的综合证据。

IF 6.5 1区 医学 Q1 PSYCHIATRY
Jiahao Liang, Yiji Wang
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引用次数: 0

摘要

背景:虽然先前的研究已经建立了社会互动和行为调整之间的关系,但尚不清楚早期社会互动的各个方面是否与行为问题有独特的关系,以及每个方面在预测内化和外化问题方面的相对重要性。采用传统的理论驱动和新颖的数据驱动的视角,本纵向研究同时评估了学龄前母亲-孩子、教师-孩子和同伴互动在预测小学早期内化和外化问题中的作用。方法:在36个月时,对儿童与母亲、老师和同伴的社会互动质量进行观察和编码(N = 1028)。母亲们后来报告了孩子在一年级时的内化和外化问题。理论驱动的结构方程建模(SEM)和数据驱动的机器学习模型(即随机森林和支持向量机)分别进行数据分析。结果:结果表明,机器学习模型,特别是支持向量机,在模型性能上优于SEM。在预测因子的相对重要性方面,扫描电镜显示,在同时考虑师生互动和母子互动的情况下,早期同伴互动指标对小学早期行为问题的预测是唯一的。机器学习模型一致表明,早期同伴互动的指标具有最高的特征重要性,并且是儿童随后行为调整的最高预测因素之一。结论:这些发现为更好地理解学龄前儿童的社会互动与后来的小学早期行为调整之间的纵向联系提供了理论和数据驱动方法的证据汇集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early social interactions and young school-aged children's behavioral problems: Converging evidence from theory- and data-driven approaches.

Background: Although prior studies have established the relation between social interactions and behavioral adjustment, it remains unclear whether aspects of early social interactions are uniquely related to behavioral problems and the relative importance of each in predicting internalizing and externalizing problems. Using traditional theory-driven and novel data-driven perspectives, this longitudinal study simultaneously evaluated the role of preschool mother-child, teacher -child, and peer interactions in predicting internalizing and externalizing problems in early grade school.

Methods: At 36 months, the quality of children's social interactions with mothers, teachers, and peers were observed and coded (N = 1,028). Mothers later reported children's internalizing and externalizing problems in first grade. Theory-driven structural equation modeling (SEM) and data-driven machine learning models (i.e., random forests and support vector machines) were performed separately for data analysis.

Results: The results showed that machine learning models, particularly support vector machines, outperformed SEM in model performance. Regarding the relative importance of predictors, SEM suggested that indicators of early peer interactions uniquely predicted behavioral problems in early grade school when those of teacher-child and mother-child interactions were considered simultaneously. Machine learning models consistently demonstrated that indicators of early peer interactions had the highest feature importance and were among the highest ranking predictors of children's subsequent behavioral adjustment.

Conclusions: The findings contribute converging evidence from theory- and data-driven approaches to better understand the longitudinal associations between preschoolers' social interactions and later behavioral adjustments in early grade school.

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来源期刊
CiteScore
13.80
自引率
5.30%
发文量
169
审稿时长
1 months
期刊介绍: The Journal of Child Psychology and Psychiatry (JCPP) is a highly regarded international publication that focuses on the fields of child and adolescent psychology and psychiatry. It is recognized for publishing top-tier, clinically relevant research across various disciplines related to these areas. JCPP has a broad global readership and covers a diverse range of topics, including: Epidemiology: Studies on the prevalence and distribution of mental health issues in children and adolescents. Diagnosis: Research on the identification and classification of childhood disorders. Treatments: Psychotherapeutic and psychopharmacological interventions for child and adolescent mental health. Behavior and Cognition: Studies on the behavioral and cognitive aspects of childhood disorders. Neuroscience and Neurobiology: Research on the neural and biological underpinnings of child mental health. Genetics: Genetic factors contributing to the development of childhood disorders. JCPP serves as a platform for integrating empirical research, clinical studies, and high-quality reviews from diverse perspectives, theoretical viewpoints, and disciplines. This interdisciplinary approach is a key feature of the journal, as it fosters a comprehensive understanding of child and adolescent mental health. The Journal of Child Psychology and Psychiatry is published 12 times a year and is affiliated with the Association for Child and Adolescent Mental Health (ACAMH), which supports the journal's mission to advance knowledge and practice in the field of child and adolescent mental health.
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