IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v362y2024ics0277953624008967.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953624008967
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.socscimed.2024.117442?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    4. 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.
    5. 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.
    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. 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..
    8. 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).
    9. 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.
    10. 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.
    11. repec:pri:indrel:dsp01m613mx58m is not listed on IDEAS
    12. 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.
    13. 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.
    14. 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.
    15. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    2. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    3. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    4. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    5. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    6. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    7. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
    8. Kai Feng & Han Hong, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Apr 2024.
    9. Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022. "Uncovering interpretable potential confounders in electronic medical records," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    10. Keyon Vafa & Susan Athey & David M. Blei, 2024. "Estimating Wage Disparities Using Foundation Models," Papers 2409.09894, arXiv.org, revised Apr 2025.
    11. Brian Cho & Ana-Roxana Pop & Ariel Evnine & Nathan Kallus, 2025. "SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement," Papers 2503.12760, arXiv.org, revised Mar 2025.
    12. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    13. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    14. Gazeaud, Jules & Khan, Nausheen & Mvukiyehe, Eric & Sterck, Olivier, 2023. "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia," Journal of Development Economics, Elsevier, vol. 165(C).
    15. Skoufias,Emmanuel & Vinha,Katja Pauliina, 2020. "Child Stature, Maternal Education, and Early Childhood Development," Policy Research Working Paper Series 9396, The World Bank.
    16. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Papers 2201.07072, arXiv.org, revised Apr 2023.
    17. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
    18. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    19. Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2023. "High dimensional semiparametric moment restriction models," Journal of Econometrics, Elsevier, vol. 232(2), pages 320-345.
    20. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2023. "Sovereign Risk and Economic Complexity: Machine Learning Insights on Causality and Prediction," IREA Working Papers 202315, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:362:y:2024:i:c:s0277953624008967. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.