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Understanding cultural stress and mental health among Latinos in the us: probabilistic omnidirectional inference model

Author

Listed:
  • Sumeyra Sahbaz

    (The University of Texas at Austin)

  • Kazim Topuz

    (The University of Tulsa)

  • Seth J. Schwartz

    (The University of Texas at Austin)

  • Pablo Montero-Zamora

    (The University of Texas at Austin)

Abstract

Recent evidence suggests that cultural stress might predict Latinos’ mental health outcomes. Yet, how two sources of cultural stress such as discrimination and negative context of reception are related to anxiety and depression is not well understood. This study aimed to investigate the impact of discrimination, negative context of reception, and demographic factors on anxiety and depression levels among 1426 Latino adults in the United States. Using two novel online simulators based on Bayesian Belief Networks, we explored how variations in these independent variables influence mental health outcomes. Our findings reveal that discrimination and negative context of reception significantly affect anxiety and depression, with discrimination being a stronger predictor. Generational status also played a key role, with second-generation Latinos experiencing worse mental health compared to the first generation. The item “To what extent do you feel that Americans have something against you?” was identified as the strongest predictor of mental health. Probabilistic machine learning approach allowed for the examination of complex interactions and non-linear relationships, providing deeper insights into the dynamics of cultural stressors and mental health. These findings suggest that addressing discrimination and negative context of reception could be vital for interventions aimed at improving the mental health of Latino populations. The use of online simulation tools in this research offers a novel method for subject-matter experts to explore and understand the intricate relationships between cultural stressors and mental health, potentially informing future prevention strategies.

Suggested Citation

  • Sumeyra Sahbaz & Kazim Topuz & Seth J. Schwartz & Pablo Montero-Zamora, 2025. "Understanding cultural stress and mental health among Latinos in the us: probabilistic omnidirectional inference model," Annals of Operations Research, Springer, vol. 346(3), pages 2461-2481, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:3:d:10.1007_s10479-024-06448-w
    DOI: 10.1007/s10479-024-06448-w
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    References listed on IDEAS

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    1. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    2. Abdurrezzak Sener & Mehmet Barut & Ali Dag & Mehmet Bayram Yildirim, 2021. "Impact of commitment, information sharing, and information usage on supplier performance: a Bayesian belief network approach," Annals of Operations Research, Springer, vol. 303(1), pages 125-158, August.
    3. Kazim Topuz & Brett D. Jones & Sumeyra Sahbaz & Murad Moqbel, 2021. "Methodology to combine theoretical knowledge with a data-driven probabilistic graphical model," Journal of Business Analytics, Taylor & Francis Journals, vol. 4(2), pages 125-139, July.
    4. Jackson, J.S. & Knight, K.M. & Rafferty, J.A., 2010. "Race and unhealthy behaviors: Chronic stress, the HPA Axis, and physical and mental health disparities over the life course," American Journal of Public Health, American Public Health Association, vol. 100(5), pages 933-939.
    5. Topuz, Kazim & Urban, Timothy L. & Yildirim, Mehmet B., 2024. "A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach," European Journal of Operational Research, Elsevier, vol. 317(2), pages 341-351.
    6. Boardman, Jason D. & Alexander, Kari B., 2011. "Stress trajectories, health behaviors, and the mental health of black and white young adults," Social Science & Medicine, Elsevier, vol. 72(10), pages 1659-1666, May.
    7. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    8. Abroon Qazi & Mecit Can Emre Simsekler & Steven Formaneck, 2023. "Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation," Annals of Operations Research, Springer, vol. 322(1), pages 241-272, March.
    9. Kim, Ji-Hyun, 2009. "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3735-3745, September.
    10. Enes Eryarsoy & Kazim Topuz & Cenk Demiroglu, 2024. "Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach," Annals of Operations Research, Springer, vol. 335(2), pages 761-795, April.
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