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

• 成果类型: Article
• 署名作者: Baird, Sarah; Panlilio, Raphael; Seager, Jennifer; Smith, Stephanie; Wydick, Bruce
• 署名单位: George Washington University; Innovations for Poverty Action (IPA); University of San Francisco
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2022.102822
• 发表日期: 2022
• 关键词: Refugees; Syrian conflict; Lasso regression; Psychological trauma; Children and adolescence; Drawings

摘要

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.