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A Scoping Review on Adverse Childhood Experiences Studies Using Latent Class Analysis: Strengths and Challenges

潜在类模型 童年不良经历 心理干预 心理学 斯科普斯 包裹体(矿物) 梅德林 心理健康 社会心理学 政治学 精神科 统计 数学 法学
作者
Xiafei Wang,Linghua Jiang,Lauren Barry,Xiaoyan Zhang,Sara A. Vasilenko,Ryan D. Heath
出处
期刊:Trauma, Violence, & Abuse [SAGE Publishing]
卷期号:25 (2): 1695-1708 被引量:45
标识
DOI:10.1177/15248380231192922
摘要

Adverse childhood experiences (ACEs) studies reveal the profound impacts of experiencing trauma and hardships in childhood. However, the cumulative risk approach of treating ACEs obscures the heterogeneity of ACEs and their consequences, making actionable interventions impossible. latent class analysis (LCA) has increasingly been used to address these concerns by identifying underlying subgroups of people who experience distinctive patterns of co-occurring ACEs. Though LCA has its strengths, the existing research produces few comparable findings because LCA results are dependent on ACEs measures and indicators, which vary widely by study. Therefore, a scoping review of ACEs studies using LCA that focuses on ACEs measures, indicators, and findings is needed to inform the field. Following Arksey and O'Malley's five-stage scoping review methodological framework, we first identified 211 articles from databases of EBSCOhost, PubMed, and Scopus using "adverse childhood experiences" for title search and "latent class analysis" for abstract search. Based on the inclusion criteria of peer-reviewed articles written in English published from 2012 to 2022 and the exclusion criteria of nonempirical studies and the LCA not analyzing ACEs, we finally selected 58 articles in this scoping review. Results showed LCA has been increasingly endorsed in the ACEs research community to examine the associations between ACEs and human health and well-being across culturally diverse populations. LCA overcame the limitations of the traditional methods by revealing specific ACEs clusters that exert potent effects on certain outcomes. However, the arbitrary nature of selecting ACEs indicators, measures, and the limited use of theory impedes the field from moving forward.
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