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

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

Journal of Development Economics, 2022, vol. 156, issue C

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.

Keywords: Refugees; Syrian conflict; Lasso regression; Psychological trauma; Children and adolescence; Drawings (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:156:y:2022:i:c:s0304387822000062

DOI: 10.1016/j.jdeveco.2022.102822

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