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Identifying psychological trauma among Syrian refugee children for early intervention: Analyzing digitized drawings using machine learning

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  • Baird, Sarah
  • Panlilio, Raphael
  • Seager, Jennifer
  • Smith, Stephanie
  • Wydick, Bruce

Abstract

Nearly 5.6 million Syrian refugees have been displaced by the country's civil war, of which roughly half are children. A digital analysis of features in children's drawings potentially represents a rapid, cost-effective, and non-invasive method for collecting information about children's mental health. Using data collected from free drawings and self-portraits from 2480 Syrian refugee children in Jordan across two distinct datasets, we use LASSO machine-learning techniques to understand the relationship between psychological trauma among refugee children and digitally coded features of their drawings. We find that children's drawing features retained using LASSO are consistent with historical correlations found between specific drawing features and psychological distress in clinical settings. We then use drawing features within LASSO to predict exposure to violence and refugee integration into host countries, with findings consistent with anticipated associations. Results serve as a proof-of-concept for the potential use of children's drawings as a diagnostic tool in human crisis settings.

Suggested Citation

  • Baird, Sarah & Panlilio, Raphael & Seager, Jennifer & Smith, Stephanie & Wydick, Bruce, 2022. "Identifying psychological trauma among Syrian refugee children for early intervention: Analyzing digitized drawings using machine learning," Journal of Development Economics, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:deveco:v:156:y:2022:i:c:s0304387822000062
    DOI: 10.1016/j.jdeveco.2022.102822
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    References listed on IDEAS

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    1. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    2. Paul Glewwe & Phillip H. Ross & Bruce Wydick, 2018. "Developing Hope among Impoverished Children: Using Child Self-Portraits to Measure Poverty Program Impacts," Journal of Human Resources, University of Wisconsin Press, vol. 53(2), pages 330-355.
    3. Jonathan de Quidt & Johannes Haushofer, 2016. "Depression for Economists," NBER Working Papers 22973, National Bureau of Economic Research, Inc.
    4. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
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    Cited by:

    1. Britta Rude, 2023. "Ending Statelessness for Displaced Children: Impact on Early Childhood Education," ifo Working Paper Series 401, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

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