IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v13y2024i5p255-d1391196.html
   My bibliography  Save this article

Enhancing Mental Health Predictions: A Gradient Boosted Model for Sri Lankan Camp Refugees

Author

Listed:
  • Indranil Sahoo

    (Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Elizabeth Amona

    (Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Miriam Kuttikat

    (School of Social Work, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • David Chan

    (Department of Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284, USA)

Abstract

This study explores the mental health challenges encountered by Sri Lankan camp refugees, a population often marginalized in mental health research, and analyzes a range of factors including socio-demographic characteristics, living conditions in camps, and psychological variables. In quantitative mental health research, linear regression serves as a conventional approach for assessing the influence of diverse factors on mental health outcomes. However, this method fails to accommodate non-linear relationships between mental health variables and predictors and relies on stringent model assumptions that often do not align with real-world conditions. This study introduces a model-agnostic, advanced machine learning/artificial intelligence (ML/AI) technique, glmboost, as a viable alternative to linear regression. The glmboost algorithm is capable of fitting non-linear prediction models while also conducting variable selection. Moreover, the coefficients obtained from the glmboost model retain the same interpretability as those derived from linear regression. While the glmboost model identifies several key factors including post-migration living difficulties, post-traumatic stress disorder, difficulty in sleeping, poor family functioning, and lower informal support from families as markers of declining mental well-being among the Sri Lankan refugees, the linear regression overlooks vital predictors such as family functioning and family support, highlighting the importance of utilizing advanced ML/AI techniques like glmboost to comprehensively capture complex relationships between predictor variables and mental health outcomes among refugee populations. Thus, by introducing a novel, data-driven approach to mental health risk assessment, this study paves the way for more precise and efficient analyses and interventions in refugee settings, with the potential for improved resource allocation and personalized support, thus revolutionizing mental health service delivery in challenging environments. Additionally, it contributes to the academic discussion on refugee mental health while emphasizing the pivotal role of advanced data analytics in addressing complex health issues within humanitarian contexts.

Suggested Citation

  • Indranil Sahoo & Elizabeth Amona & Miriam Kuttikat & David Chan, 2024. "Enhancing Mental Health Predictions: A Gradient Boosted Model for Sri Lankan Camp Refugees," Social Sciences, MDPI, vol. 13(5), pages 1-14, May.
    • Handle: RePEc:gam:jscscx:v:13:y:2024:i:5:p:255-:d:1391196
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/13/5/255/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-0760/13/5/255/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miriam Kuttikat, 2022. "Proverbs and Gender: The Interface between Proverbs and Human Relations in a Refugee Camp," International Journal of Social Science Studies, Redfame publishing, vol. 10(6), pages 6774-6774, November.
    2. Walther, Lena & Fuchs, Lukas M. & Schupp, Jürgen & Scheve, Christian von, 2020. "Living Conditions and the Mental Health and Well-being of Refugees : Evidence from a Large-Scale German Survey," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 22, pages 903-913.
    3. Mathilde Sengoelge & Alexander Nissen & Øivind Solberg, 2022. "Post-Migration Stressors and Health-Related Quality of Life in Refugees from Syria Resettled in Sweden," IJERPH, MDPI, vol. 19(5), pages 1-14, February.
    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. Jaschke Philipp & Sulin Sardoschau & Marco Tabellini, 2021. "Scared Straight? Threat and Assimilation of Refugees in Germany," RF Berlin - CReAM Discussion Paper Series 2136, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    2. Keita, Sekou & Schewe, Paul, 2021. "Out of sight, out of mind? Terror in the home country, family reunification options, and the well-being of refugees," World Development, Elsevier, vol. 146(C).
    3. Jacobsen Jannes & Krieger Magdalena & Schikora Felicitas & Schupp Jürgen, 2021. "Growing Potentials for Migration Research using the German Socio-Economic Panel Study," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 527-549, August.
    4. Eva Spiritus-Beerden & An Verelst & Ines Devlieger & Nina Langer Primdahl & Fábio Botelho Guedes & Antonio Chiarenza & Stephanie De Maesschalck & Natalie Durbeej & Rocío Garrido & Margarida Gaspar de , 2021. "Mental Health of Refugees and Migrants during the COVID-19 Pandemic: The Role of Experienced Discrimination and Daily Stressors," IJERPH, MDPI, vol. 18(12), pages 1-14, June.
    5. Temesghen Gebresilassie & Claudia Beiersmann & Sandra Ziegler & Verena Keck & Yonas Semere Kidane & Albrecht Jahn & Janine Benson-Martin, 2022. "Mental Wellbeing and Social Resilience of Eritrean Refugees Living in Germany," IJERPH, MDPI, vol. 19(17), pages 1-16, September.
    6. Sebastian Leitner, 2023. "Development of Mental Distress of Refugees in Austria During their Economic and Social Integration in 2017-2022," wiiw Working Papers 233, The Vienna Institute for International Economic Studies, wiiw.
    7. Liyan Huang & Rosli Said & Hong Ching Goh & Yu Cao, 2023. "The Residential Environment and Health and Well-Being of Chinese Migrant Populations: A Systematic Review," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    8. Jaschke, Philipp & Sardoschau, Sulin & Tabellini, Marco, 2021. "Scared Straight? Threat and Assimilation of Refugees in Germany," IZA Discussion Papers 14962, Institute of Labor Economics (IZA).
    9. Alfonso Urzúa & Diego Henríquez & Alejandra Caqueo-Urízar & Rodrigo Landabur, 2021. "Ethnic Identity and Collective Self-Esteem Mediate the Effect of Anxiety and Depression on Quality of Life in a Migrant Population," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    10. Löbel, Lea-Maria & Jacobsen, Jannes, 2021. "Waiting for kin: a longitudinal study of family reunification and refugee mental health in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 47(13).
    11. Priscilla Paola Severo & Leonardo B. Furstenau & Michele Kremer Sott & Danielli Cossul & Mariluza Sott Bender & Nicola Luigi Bragazzi, 2021. "Thirty Years of Human Rights Study in the Web of Science Database (1990–2020)," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
    12. Elżbieta Ociepa-Kicińska & Małgorzata Gorzałczyńska-Koczkodaj, 2022. "Forms of Aid Provided to Refugees of the 2022 Russia–Ukraine War: The Case of Poland," IJERPH, MDPI, vol. 19(12), pages 1-17, June.
    13. Mathilde Sengoelge & Alexander Nissen & Øivind Solberg, 2022. "Post-Migration Stressors and Health-Related Quality of Life in Refugees from Syria Resettled in Sweden," IJERPH, MDPI, vol. 19(5), pages 1-14, February.

    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:gam:jscscx:v:13:y:2024:i:5:p:255-:d:1391196. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.