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A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine

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  • Asanov, Anastasiya-Mariya
  • Asanov, Igor
  • Buenstorf, Guido

Abstract

Armed conflicts increase distress levels among affected populations, particularly impacting refugees who often face barriers to accessing psychological support. We evaluate an online version of a previously tested in-person and endorsed for online adaptation by the WHO Self-Help Plus (SH+) program among Ukrainian refugees dispersed across 17 countries, internally displaced and not displaced Ukrainians. This is the first randomized controlled trial to test an online psychological intervention simultaneously on refugees, internally displaced, and non-displaced conflict-affected populations. This study is an online two-arm, individually randomized controlled trial among participants above 18 years old in Ukraine or EU countries who were randomly assigned to receive either the Self-Help Online (SHO) intervention and passive informational resource or the passive informational resource alone. We recruited 652 participants starting the program on July 7th, 2022. The analysis focused on 292 participants who completed the final survey one week after the end of the program. Results indicated significant distress reduction among refugees (β −2.16, 95% CI −4.17 to −0.16; p = 0.03; d −0.47) but not among internally displaced in Ukraine (β 0.56, 95% CI −1.1 to 2.99; p = 0.17; d 0.2) or non-displaced participants in Ukraine (β 0.2, 95% CI −0.95 to 1.35; p = 0.73; d 0.08). The effect size in stress reduction for refugees was comparable to other similar interventions but with lower average costs. The average cost per participant was €11, with €46.16 for each benefiting (refugee) participant, suggesting cost-effectiveness for scale-up. These findings suggest that Self-Help Online is an effective psychological intervention for reducing stress among geographically dispersed refugees at a low cost. We also find that the online delivery format of psychological interventions is feasible for internally displaced and non-displaced conflict-affected populations.

Suggested Citation

  • Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido, 2024. "A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine," Social Science & Medicine, Elsevier, vol. 362(C).
    • Handle: RePEc:eee:socmed:v:362:y:2024:i:c:s0277953624008967
    DOI: 10.1016/j.socscimed.2024.117442
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    References listed on IDEAS

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    1. Ruhnke, Simon A. & Hertner, Laura & Köhler, Judith & Kluge, Ulrike, 2024. "Social ecological determinants of the mental distress among Syrian refugees in Lebanon and Turkey: A transnational perspective," Social Science & Medicine, Elsevier, vol. 346(C).
    2. Lindert, Jutta & Ehrenstein, Ondine S. von & Priebe, Stefan & Mielck, Andreas & Brähler, Elmar, 2009. "Depression and anxiety in labor migrants and refugees - A systematic review and meta-analysis," Social Science & Medicine, Elsevier, vol. 69(2), pages 246-257, July.
    3. Jeffrey R. Kling & Jeffrey B. Liebman, 2004. "Experimental Analysis of Neighborhood Effects on Youth," Working Papers 1, Princeton University, Department of Economics, Industrial Relations Section..
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    5. Igor Asanov & Anastasiya-Mariya Asanov & Thomas Åstebro & Guido Buenstorf & Bruno Crépon & David McKenzie & Francisco Pablo Flores T. & Mona Mensmann & Mathis Schulte, 2023. "System-, teacher-, and student-level interventions for improving participation in online learning at scale in high schools," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(30), pages 2216686120-, July.
    6. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    7. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    8. Emily Breza & Fatima Cody Stanford & Marcela Alsan & M. D. Ph. D. & Burak Alsan & Abhijit Banerjee & Arun G. Chandrasekhar & Sarah Eichmeyer & Traci Glushko & Paul Goldsmith-Pinkham & Kelly Holland & , 2021. "Doctors and Nurses Social Media Ads Reduced Holiday Travel and COVID-19 infections: A cluster randomized controlled trial in 13 States," Papers 2106.11012, arXiv.org.
    9. Davies, Elwyn & Deffebach, Peter & Iacovone, Leonardo & McKenzie, David, 2024. "Training microentrepreneurs over Zoom: Experimental evidence from Mexico," Journal of Development Economics, Elsevier, vol. 167(C).
    10. Emily Breza & Fatima Cody Stanford & Marcella Alsan & Burak Alsan & Abhijit Banerjee & Arun G. Chandrasekhar & Sarah Eichmeyer & Traci Glushko & Paul Goldsmith-Pinkham & Kelly Holland & Emily Hoppe & , 2021. "Doctors' and Nurses' Social Media Ads Reduced Holiday Travel and COVID-19 Infections: A Cluster Randomized Controlled Trial," NBER Working Papers 29021, National Bureau of Economic Research, Inc.
    11. Alexander J. Ohnmacht & Arndt Stahler & Sebastian Stintzing & Dominik P. Modest & Julian W. Holch & C. Benedikt Westphalen & Linus Hölzel & Marisa K. Schübel & Ana Galhoz & Ali Farnoud & Minhaz Ud-Dea, 2023. "The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    12. Das, Jishnu & Do, Quy-Toan & Friedman, Jed & McKenzie, David & Scott, Kinnon, 2007. "Mental health and poverty in developing countries: Revisiting the relationship," Social Science & Medicine, Elsevier, vol. 65(3), pages 467-480, August.
    13. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    14. repec:pri:indrel:dsp01m613mx58m is not listed on IDEAS
    15. Richard A Bryant & Alison Schafer & Katie S Dawson & Dorothy Anjuri & Caroline Mulili & Lincoln Ndogoni & Phiona Koyiet & Marit Sijbrandij & Jeannette Ulate & Melissa Harper Shehadeh & Dusan Hadzi-Pav, 2017. "Effectiveness of a brief behavioural intervention on psychological distress among women with a history of gender-based violence in urban Kenya: A randomised clinical trial," PLOS Medicine, Public Library of Science, vol. 14(8), pages 1-20, August.
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