IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i15p7846-d600761.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/15/7846/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/15/7846/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. repec:aph:ajpbhl:10.2105/ajph.2017.303907_8 is not listed on IDEAS
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    3. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    4. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    5. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
    6. Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    7. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    8. Eunsil Seok & Akhgar Ghassabian & Yuyan Wang & Mengling Liu, 2024. "Statistical Methods for Modeling Exposure Variables Subject to Limit of Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(2), pages 435-458, July.
    9. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    10. Georges Steffgen & Philipp E. Sischka & Martha Fernandez de Henestrosa, 2020. "The Quality of Work Index and the Quality of Employment Index: A Multidimensional Approach of Job Quality and Its Links to Well-Being at Work," IJERPH, MDPI, vol. 17(21), pages 1-31, October.
    11. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    12. Esef Hakan Toytok & Sungur Gürel, 2019. "Does Project Children’s University Increase Academic Self-Efficacy in 6th Graders? A Weak Experimental Design," Sustainability, MDPI, vol. 11(3), pages 1-12, February.
    13. J M van Niekerk & M C Vos & A Stein & L M A Braakman-Jansen & A F Voor in ‘t holt & J E W C van Gemert-Pijnen, 2020. "Risk factors for surgical site infections using a data-driven approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    14. Stefkovics, Ádám & Krekó, Péter & Koltai, Júlia, 2024. "When reality knocks on the door. The effect of conspiracy beliefs on COVID-19 vaccine acceptance and the moderating role of experience with the virus," Social Science & Medicine, Elsevier, vol. 356(C).
    15. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    16. Lara Jehi & Xinge Ji & Alex Milinovich & Serpil Erzurum & Amy Merlino & Steve Gordon & James B Young & Michael W Kattan, 2020. "Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    17. Matthew Carli & Mary H. Ward & Catherine Metayer & David C. Wheeler, 2022. "Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression," IJERPH, MDPI, vol. 19(3), pages 1-17, January.
    18. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    19. Tsai, Tsung-Han, 2016. "A Bayesian Approach to Dynamic Panel Models with Endogenous Rarely Changing Variables," Political Science Research and Methods, Cambridge University Press, vol. 4(3), pages 595-620, September.
    20. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    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:jijerp:v:18:y:2021:i:15:p:7846-:d:600761. 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.