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Beyond the income‐achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low‐income youth
by
McLaughlin, Katie A.
, Sadikova, Ekaterina
, Rakesh, Divyangana
in
ABCD study
/ childhood and adolescence
/ cognitive function
/ Original
/ poverty
/ resilience
/ socioeconomic status
2025
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Beyond the income‐achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low‐income youth
by
McLaughlin, Katie A.
, Sadikova, Ekaterina
, Rakesh, Divyangana
in
ABCD study
/ childhood and adolescence
/ cognitive function
/ Original
/ poverty
/ resilience
/ socioeconomic status
2025
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Do you wish to request the book?
Beyond the income‐achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low‐income youth
by
McLaughlin, Katie A.
, Sadikova, Ekaterina
, Rakesh, Divyangana
in
ABCD study
/ childhood and adolescence
/ cognitive function
/ Original
/ poverty
/ resilience
/ socioeconomic status
2025
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Beyond the income‐achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low‐income youth
Journal Article
Beyond the income‐achievement gap: The role of individual, family, and environmental factors in cognitive resilience among low‐income youth
2025
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Overview
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. Machine learning based prediction of cognitive resilience among low income youth.
Publisher
Wiley
Subject
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