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Conspiracy Theories, Psychological Distress, and Sympathy for Violent Radicalization in Young Adults during the COVID-19 Pandemic: A Cross-Sectional Study

Author

Listed:
  • Anna Levinsson

    (Division of Social and Cultural Psychiatry, McGill University, CLSC Parc-Extension, 7085 Hutchison, Montréal, QC H3N 1Y9, Canada)

  • Diana Miconi

    (Division of Social and Cultural Psychiatry, McGill University, CLSC Parc-Extension, 7085 Hutchison, Montréal, QC H3N 1Y9, Canada)

  • Zhiyin Li

    (Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Ave. W, Montreal, QC H3A 1A2, Canada)

  • Rochelle L. Frounfelker

    (Division of Social and Cultural Psychiatry, McGill University, CLSC Parc-Extension, 7085 Hutchison, Montréal, QC H3N 1Y9, Canada)

  • Cécile Rousseau

    (Division of Social and Cultural Psychiatry, McGill University, CLSC Parc-Extension, 7085 Hutchison, Montréal, QC H3N 1Y9, Canada)

Abstract

The COVID-19 pandemic has spread uncertainty, promoted psychological distress, and fueled interpersonal conflict. The concomitant upsurge in endorsement of COVID-19 conspiracy theories is worrisome because they are associated with both non-adherence to public health guidelines and intention to commit violence. This study investigates associations between endorsement of COVID-19 conspiracy theories, support for violent radicalization (VR) and psychological distress among young adults in Canada. We hypothesized that (a) endorsement of COVID-19 conspiracy theories is positively associated with support for VR, and (b) psychological distress modifies the relationship between COVID-19 conspiracy theories and support for VR. A total of 6003 participants aged 18–35 years old residing in four major Canadian cities completed an online survey between 16 October 2020 and 17 November 2020, that included questions about endorsement of COVID-19 conspiracy theories, support for VR, psychological distress, and socio-economic status. Endorsement of conspiracy theories was associated with support for VR in multivariate regression (β = 0.88, 95% confidence interval (CI) 0.80–0.96). There is a significant interaction effect between endorsement of COVID-19 conspiracy theories and psychological distress (β = 0.49, 95% CI 0.40–0.57). The magnitude of the association was stronger in individuals reporting high psychological distress (β = 1.36, 95% CI 1.26–1.46) compared to those reporting low psychological distress (β = 0.47, 95% CI 0.35–0.59). The association between endorsement of COVID-19 conspiracy theories and VR represents a public health challenge requiring immediate attention. The interaction with psychological distress suggests that policy efforts should combine communication and psychological strategies to mitigate the legitimation of violence.

Suggested Citation

  • Anna Levinsson & Diana Miconi & Zhiyin Li & Rochelle L. Frounfelker & Cécile Rousseau, 2021. "Conspiracy Theories, Psychological Distress, and Sympathy for Violent Radicalization in Young Adults during the COVID-19 Pandemic: A Cross-Sectional Study," IJERPH, MDPI, vol. 18(15), pages 1-12, July.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:7846-:d:600761
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Kamaldeep Bhui & Nasir Warfa & Edgar Jones, 2014. "Is Violent Radicalisation Associated with Poverty, Migration, Poor Self-Reported Health and Common Mental Disorders?," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    3. Jude Mary Cénat & Rose Darly Dalexis & Cyrille Kossigan Kokou-Kpolou & Joana N. Mukunzi & Cécile Rousseau, 2020. "Social inequalities and collateral damages of the COVID-19 pandemic: when basic needs challenge mental health care," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(6), pages 717-718, July.
    4. Torbjørn Moum, 1998. "Mode of administration and interviewer effects in self-reported symptoms of anxiety and depression," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 45(1), pages 279-318, November.
    5. Wynia, M.K. & Eisenman, D. & Hanfling, D., 2017. "Ideologically motivated violence: A public health approach to prevention," American Journal of Public Health, American Public Health Association, vol. 107(8), pages 1244-1246.
    6. repec:aph:ajpbhl:10.2105/ajph.2017.303907_8 is not listed on IDEAS
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    Cited by:

    1. van Mulukom, Valerie & Pummerer, Lotte J. & Alper, Sinan & Bai, Hui & Čavojová, Vladimíra & Farias, Jessica & Kay, Cameron S. & Lazarevic, Ljiljana B. & Lobato, Emilio J.C. & Marinthe, Gaëlle & Pavela, 2022. "Antecedents and consequences of COVID-19 conspiracy beliefs: A systematic review," Social Science & Medicine, Elsevier, vol. 301(C).
    2. Zhaoxie Zeng & Yi Ding & Yue Zhang & Yongyu Guo, 2022. "What Breeds Conspiracy Theories in COVID-19? The Role of Risk Perception in the Belief in COVID-19 Conspiracy," IJERPH, MDPI, vol. 19(9), pages 1-11, April.

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