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Machine learning for identifying caregiving adversities associated with greatest risk for mental health problems in children
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Description: Substantial heterogeneity in crEA experiences, mental health outcomes, and research approaches, coupled with computational limitations of common statistics, pose a major challenge to obtaining replicable and generalizable findings in the endeavor of identifying mental health correlates of early adversity. To improve replicability, the current study leveraged a data-driven approach to identify the most robust links between crEAs and mental health in children (6-12 years-old) on the basis of generalizability and external validity. Findings advance ongoing debates as to why and when early caregiving adversity engenders risk and resilience for psychopathology.
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