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内化与外化症状学发展轨迹的异质性:与风险因素和保护因素的关联

Heterogeneity in Developmental Trajectories of Internalizing and Externalizing Symptomatology: Associations with Risk and Protective Factors.

作者信息

Brieant A, Cai T, Ip K I, Holt-Gosselin B, Gee D G

机构信息

Department of Psychological Science, University of Vermont, Burlington, VT, 05402, USA.

Institute of Child Development, University of Minnesota, Minneapolis, MN, 55455, USA.

出版信息

Child Psychiatry Hum Dev. 2025 Jan 9. doi: 10.1007/s10578-024-01804-0.

DOI:10.1007/s10578-024-01804-0
PMID:39786680
Abstract

Among a large sample of youth (9-10 years old at baseline) from the Adolescent Brain Cognitive Development (ABCD) Study® (n = 11,661) we modeled trajectories of psychopathology over three years and associated risk and protective factors. Growth mixture modeling characterized latent classes with distinct psychopathology trajectories. Results indicated four different internalizing trajectories: a high-decreasing class, a moderate-decreasing class, a moderate-increasing class, and a low-stable class. There were also four externalizing trajectories: a moderate-decreasing class, a high-decreasing class, a moderate-increasing class, and a low-decreasing class. We used parallel process growth analysis to examine the co-development of internalizing and externalizing symptoms and characterized five trajectory classes with distinct patterns of co-development. These classes were differentially associated with negative life events, neighborhood safety, and parental acceptance. Together, the findings characterize general developmental patterns of psychopathology, quantify the proportion of youth that follow each pattern, and identify key predictors that discriminate these patterns.

摘要

在青少年大脑认知发展(ABCD)研究®的一大群青少年样本(基线时9 - 10岁,n = 11,661)中,我们对三年间的精神病理学轨迹以及相关的风险和保护因素进行了建模。增长混合模型刻画了具有不同精神病理学轨迹的潜在类别。结果表明有四种不同的内化轨迹:高下降类、中度下降类、中度上升类和低稳定类。也有四种外化轨迹:中度下降类、高下降类、中度上升类和低下降类。我们使用平行过程增长分析来研究内化和外化症状的共同发展,并刻画了具有不同共同发展模式的五个轨迹类别。这些类别与负面生活事件、邻里安全和父母接纳存在差异关联。总之,这些发现刻画了精神病理学的一般发展模式,量化了遵循每种模式的青少年比例,并确定了区分这些模式的关键预测因素。

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