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Identifying Psychological Trauma among Syrian Refugee Children for Early Intervention: Analyzing Digitized Drawings using Machine Learning

  • Author(s): Baird, Sarah
  • Muz, Jennifer
  • Panlilio, Raphael
  • Smith, Stephanie
  • Wydick, Bruce
  • et al.

Published Web Location

https://doi.org/10.26085/C3WC7S
Abstract

Nearly 5.6 million Syrian refugees were displaced by the country’s civil war, of which 50% percent are children. Given the heightened risks of psychological distress for this population it is critical to efficiently and accurately assess well-being for this population for intervention. A digital analysis of features in children’s drawings potentially represents a rapid, cost-effective, and non-invasive method for collecting individual and aggregate data on children’s mental health. Using data collected from free drawings and self-portraits from over 2,500 Syrian refugee children in Jordan across two distinct datasets, we use regression and Lasso machine-learning techniques to understand the relationship between exposure to violence and different measures of psychological trauma. Our results suggest that individual drawing characteristics are strongly correlated with validated measures of psychological trauma and past exposure to violence, with child mental health declining with increased exposure to violence and improving with resettlement in host communities. These results serve as a proof-of-concept for the potential use of children’s drawings as a diagnostic tool in human crisis settings.

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