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Preschool and preadolescent antecedents of externalizing problems: a machine learning approach.

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Title: Preschool and preadolescent antecedents of externalizing problems: a machine learning approach.
Authors: Yang, Yaqi (AUTHOR), Wang, Yiji (AUTHOR)
Source: European Child & Adolescent Psychiatry. Nov2025, Vol. 34 Issue 11, p3401-3413. 13p.
Subjects: Risk assessment, Random forest algorithms, Effect sizes (Statistics), Statistical correlation, Research funding, Secondary analysis, T-test (Statistics), Statistical sampling, Interviewing, Descriptive statistics, Chi-squared test, Behavior disorders in children, Longitudinal method, Mathematical models, Research methodology, Statistics, Research, Child Behavior Checklist, Machine learning, Theory, Mother-child relationship, Inter-observer reliability, Mental depression
Abstract: This study used a data-driven approach to elucidate the relative importance of child, maternal, and mother-child dyadic characteristics in predicting externalizing problems across two critical stages, preschool and preadolescence, that mark the development of externalizing problems. Data (N = 1,364) were collected through maternal reports and observations during preschool and preadolescence. Using the random forest algorithm in machine learning, the results showed that the predictive models differed between preschool and preadolescence. For preschool antecedents of externalizing problems, maternal characteristics, such as depressive symptoms, education, and sensitivity, emerged as the most highly ranked predictors, followed by mother-child dyadic characteristics. Moreover, for preadolescent antecedents of externalizing problems, mother-child dyadic characteristics, including conflict and positive relationship, were identified as the top predictors, with maternal characteristics playing a secondary role. While child characteristics were relatively less influential across both age groups, child negative reactivity emerged as a salient predictor during preadolescence. The findings contribute data-driven evidence to elucidate the relative importance of preschool and preadolescent antecedents of externalizing problems, with maternal characteristics playing a central role in early childhood and mother-child dynamics becoming most important during preadolescence. Interventions targeting externalizing problems should be developmentally sensitive, with preschool programs emphasizing maternal well-being and early relational foundations, while preadolescent programs prioritize strengthening the mother-child relationships and addressing dyadic challenges. [ABSTRACT FROM AUTHOR]
Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Preschool and preadolescent antecedents of externalizing problems: a machine learning approach.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yaqi%22">Yang, Yaqi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yiji%22">Wang, Yiji</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Child+%26+Adolescent+Psychiatry%22">European Child & Adolescent Psychiatry</searchLink>. Nov2025, Vol. 34 Issue 11, p3401-3413. 13p.
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  Data: This study used a data-driven approach to elucidate the relative importance of child, maternal, and mother-child dyadic characteristics in predicting externalizing problems across two critical stages, preschool and preadolescence, that mark the development of externalizing problems. Data (N = 1,364) were collected through maternal reports and observations during preschool and preadolescence. Using the random forest algorithm in machine learning, the results showed that the predictive models differed between preschool and preadolescence. For preschool antecedents of externalizing problems, maternal characteristics, such as depressive symptoms, education, and sensitivity, emerged as the most highly ranked predictors, followed by mother-child dyadic characteristics. Moreover, for preadolescent antecedents of externalizing problems, mother-child dyadic characteristics, including conflict and positive relationship, were identified as the top predictors, with maternal characteristics playing a secondary role. While child characteristics were relatively less influential across both age groups, child negative reactivity emerged as a salient predictor during preadolescence. The findings contribute data-driven evidence to elucidate the relative importance of preschool and preadolescent antecedents of externalizing problems, with maternal characteristics playing a central role in early childhood and mother-child dynamics becoming most important during preadolescence. Interventions targeting externalizing problems should be developmentally sensitive, with preschool programs emphasizing maternal well-being and early relational foundations, while preadolescent programs prioritize strengthening the mother-child relationships and addressing dyadic challenges. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Child & Adolescent Psychiatry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00787-025-02754-1
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 3401
    Subjects:
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Random forest algorithms
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      – SubjectFull: Effect sizes (Statistics)
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      – SubjectFull: Statistical correlation
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      – SubjectFull: Research funding
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      – SubjectFull: T-test (Statistics)
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      – SubjectFull: Statistical sampling
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      – SubjectFull: Interviewing
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Chi-squared test
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      – SubjectFull: Behavior disorders in children
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      – SubjectFull: Machine learning
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      – SubjectFull: Mother-child relationship
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      – SubjectFull: Inter-observer reliability
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      – SubjectFull: Mental depression
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      – TitleFull: Preschool and preadolescent antecedents of externalizing problems: a machine learning approach.
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              Text: Nov2025
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