超越收入成就差距:个人、家庭和环境因素在低收入青年认知弹性中的作用

IF 3.1
JCPP advances Pub Date : 2024-12-20 DOI:10.1002/jcv2.12297
Divyangana Rakesh, Ekaterina Sadikova, Katie A. McLaughlin
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引用次数: 0

摘要

低社会经济地位与较低的认知表现和长期的成就和成功差异有关。然而,并非所有来自低收入家庭的孩子都表现出较低的认知能力。描述促进低收入家庭青年这种适应能力的因素是至关重要的。方法:我们使用来自青少年大脑认知发展研究和机器学习中收入到需求最低分位数参与者的基线数据,以确定预测低收入背景青年的流体和结晶认知弹性的因素。预测因子包括164个变量,涉及儿童特征、家庭和发展史以及环境。结果我们的模型能够可靠地预测弹性,但对于结晶认知(AUC = 0.75)比流体认知(AUC = 0.67)要准确得多。关键的预测因素包括发育因素,如出生体重和母乳喂养的持续时间,社区水平因素(如生活在特权集中的地区,参加大学先修课程),儿童自身的气质和心理健康,以及其他因素,如体育活动和课外活动的参与。我们的研究结果强调了在未来的干预工作中,从多方面提高低收入家庭儿童的认知弹性的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth

Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth

Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth

Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth

Beyond the income-achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low-income youth

Background

Low socioeconomic status is associated with lower cognitive performance and long-term disparities in achievement and success. However, not all children from low-income backgrounds exhibit lower cognitive performance. Characterizing the factors that promote such resilience in youth from low-income households is of crucial importance.

Methods

We used baseline data from participants in the lowest tertile of income-to-needs in the Adolescent Brain Cognitive Development study and machine learning to identify the factors that predict fluid and crystallized cognitive resilience among youth from low-income backgrounds. Predictors included 164 variables across child characteristics, family and developmental history, and environment.

Results

Our models were reliably able to predict resilience but were substantially more accurate for crystallized cognition (AUC = 0.75) than for fluid cognition (AUC = 0.67). Key predictors included developmental factors such as birthweight and duration of breastfeeding, neighborhood-level factors (e.g., living in concentrated privilege, enrollment in advanced placement courses), children's own temperament and mental health, and other factors such as physical activity and involvement in extracurricular activities.

Conclusion

Our findings highlight the importance of a multifaceted approach to promoting cognitive resilience among children from low-income households in future intervention work.

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