| Popis: |
Child poverty is a known risk factor for most forms of psychopathology. While broad environmental indicators such as low income confer greater risk for psychopathology on average, there is widespread heterogeneity in mental health outcomes among poverty-exposed youth that remains largely unexplained. This heterogeneity could stem from the diversity of stressors and resources (e.g., material deprivation, violence exposure, access to childcare) experienced by families–even among those with similar income levels. Thus, there is a clear need to characterize whether common patterns of environmental experiences within the context of poverty are differentially related to specific psychopathology outcomes. Thus, the current proposal leverages machine learning to test whether it is empirically justifiable to: 1) dissect the umbrella-term of “poverty” into discrete “poverty-related environments” and 2) investigate how these varying patterns of environmental risk and protective factors are linked to psychopathology. |
