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Open Access 22-06-2022 | COVID-19

The mediating role of scientifical-medical satisfaction between COVID-19 conspiracy beliefs and vaccine confidence: a two-waves structural equation model

Authors: Giuseppe Mignemi, Anna Panzeri, Umberto Granziol, Giovanni Bruno, Marco Bertamini, Giulio Vidotto, Andrea Spoto

Published in: Journal of Behavioral Medicine | Issue 1-2/2023

Abstract

Vaccine confidence has emerged as one of the most relevant psychological factors implied in the worldwide affecting the fight against COVID-19—as well as public trust in doctors, medicine, and science. Indeed, the vaccine confidence is crucial to maximize the trust in vaccines and their use for prevention, with several implications for public health. This study aimed to analyse the relationships among between vaccine confidence, conspiracy beliefs about COVID-19, and satisfaction with science and medicine in handling the COVID-19 pandemic. A longitudinal observational survey was administered to a convenience sample (n = 544; mean age 52.76 y.o., SD = 15.11; females 46.69%) from the Italian general population. A two-waves mediation model—a structural equation model technique—was used. The survey was part of a larger international project (https://osf.io/qy65b/). The model highlighted that the conspiracy beliefs about COVID-19 had a negative effect on the satisfaction with medicine and science (β = − 0.13, se = 0.03, p < .001). The latter, in turn, had a positive effect on vaccine confidence (β = 0.10, se = .05, p < .001). Interestingly, the effect of conspiracy beliefs on vaccine confidence was completely mediated by the scientifical-medical satisfaction (β = − 0.02, se = 0.01, p < .05). These results highlight how the scientifical-medical satisfaction can fully mediate the relationship between conspiracy beliefs about COVID-19 and vaccine confidence. These findings about vaccine hesitancy and confidence and disclose have implications for psychological and social interventions that could promote vaccine confidence by targeting the satisfaction with science and medicine.

Bagozzi, R. P., & Yi, Y. (1991). Multitrait-multimethod matrices in consumer research. Journal of Consumer Research, 17, 426–439. https://doi.org/10.1086/208568CrossRef

Benfante, A., Di Tella, M., Romeo, A., & Castelli, L. (2020). Traumatic stress in healthcare workers during COVID-19 pandemic: A review of the immediate impact. Frontiers in Psychology, 11, 569935. https://doi.org/10.3389/fpsyg.2020.569935CrossRefPubMedPubMedCentral

Bentler, P. M., & Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78–117. https://doi.org/10.1177/0049124187016001004CrossRef

Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.

Bruno, G., Panzeri, A., Granziol, U., Alivernini, F., Chirico, A., Galli, F., Lucidi, F., Spoto, A., Vidotto, G., & Bertamini, M. (2020). The Italian COVID-19 Psychological Research Consortium (IT C19PRC): General overview and replication of the UK study. Journal of Clinical Medicine, 10, 52. https://doi.org/10.3390/jcm10010052CrossRefPubMedPubMedCentral

Cheung, M. W. L. (2009). Comparison of methods for constructing confidence intervals of standardized indirect effects. Behavior Research Methods, 41, 425–438. https://doi.org/10.3758/BRM.41.2.425CrossRefPubMed

Cheung, W. G., & Rensvold, B. R. (2002). Evaluating goodness-of-fit indexes fortesting measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233–255.CrossRef

Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577. https://doi.org/10.1037/0021-843X.112.4.558CrossRefPubMed

Consoli, S., Rossi, A., Thompson, L. Y., Volpi, C., Mannarini, S., Castelnuovo, G., & Molinari, E. (2020). Assessing psychometric properties of the italian version of the heartland forgiveness scale. Frontiers in Psychology, 11, 1–9. https://doi.org/10.3389/fpsyg.2020.596501CrossRef

Cook, R. D., & Weisberg, S. (1982). Criticism and influence analysis in regression. Sociological Methodology, 13, 313. https://doi.org/10.2307/270724CrossRef

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27–46. https://doi.org/10.1111/J.1600-0587.2012.07348.XCrossRef

Douglas, K. M. (2021). COVID-19 conspiracy theories. Group Processes and Intergroup Relations, 24, 270–275. https://doi.org/10.1177/1368430220982068CrossRef

Epifanio, M. S., Andrei, F., Mancini, G., Agostini, F., Piombo, M. A., Spicuzza, V., Riolo, M., Lavanco, G., Trombini, E., & La Grutta, S. (2021). The impact of COVID-19 pandemic and lockdown measures on quality of life among Italian general population. Journal of Clinical Medicine, 10, 289. https://doi.org/10.3390/jcm10020289CrossRefPubMedPubMedCentral

Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532–538. https://doi.org/10.1037/a0015808CrossRef

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18, 382. https://doi.org/10.2307/3150980CrossRef

Freeston, M., Tiplady, A., Mawn, L., Bottesi, G., & Thwaites, S. (2020). Towards a model of uncertainty distress in the context of Coronavirus (COVID-19). Cognitive Behaviour Therapist. https://doi.org/10.1017/S1754470X2000029XCrossRefPubMedPubMedCentral

Fridman, A., Gershon, R., & Gneezy, A. (2021). COVID-19 and vaccine hesitancy: A longitudinal study. PLoS ONE, 16, 1–12. https://doi.org/10.1371/journal.pone.0250123CrossRef

Gibson-Miller, J., Hartman, T. K., Levita, L., Martinez, A. P., Mason, L., McBride, O., McKay, R., Murphy, J., Shevlin, M., Stocks, T. V. A., Bennett, K. M., & Bentall, R. P. (2020). Capability, opportunity, and motivation to enact hygienic practices in the early stages of the COVID-19 outbreak in the United Kingdom. British Journal of Health Psychology, 25, 856–864. https://doi.org/10.1111/bjhp.12426CrossRefPubMedPubMedCentral

Grzesiak-Feldman, M. (2013). The effect of high-anxiety situations on conspiracy thinking. Current Psychology, 32, 100–118. https://doi.org/10.1007/s12144-013-9165-6CrossRef

Harman, H. H. (1976). Modern factor analysis. University of Chicago press.

Iacobucci, D. (2010). Structural equation modeling fit indices, sample size and advanced topics. Journal of Consumer Psychology, 20, 90–98.CrossRef

ISS. (2021). Impact of COVID-19 vaccination on the risk of SARS-CoV-2 infection and hospitalization and death in Italy (27.12.2020 - 14.07.2021). English version of the ISS report - ISS (EN) - ISS. https://www.iss.it/en/web/iss-en/highlighted/-/asset_publisher/0JjjK4TivXZp/content/id/5813790?_com_liferay_asset_publisher_web_portlet_AssetPublisherPortlet_INSTANCE_0JjjK4TivXZp_redirect=https%3A%2F%2Fwww.iss.it%2Fen%2Fweb%2Fiss-en%2Fhighlighted%3Fp_p_i

Jorgensen, T. D. (2018). Package “semTools”. Useful Tools for Structural Equation Modeling NeedsCompilation no. https://cran.r-project.org/web/packages/semTools/semTools.pdf

Jost, J. T., Banaji, M. R., & Nosek, B. A. (2004). A decade of system justification theory: Accumulated evidence of conscious and unconscious bolstering of the status quo. Political Psychology, 25(6), 881–919. https://doi.org/10.1111/J.1467-9221.2004.00402.XCrossRef

Kline, R. B. (2015). Principles and practice of structural equation modeling. https://books.google.it/books?hl=it&lr=&id=Q61ECgAAQBAJ&oi=fnd&pg=PP1&dq=Kline,+R.+B.+(2010).+Principles+and+practice+of+structural+equation+mod-+eling+(3rd+ed.).+New+York,+NY:+Guilford.&ots=jFfm0sC6si&sig=WPzZy2NMpkYT_BSpA0e7wmYUflw&redir_esc=y#v=onepage.

Korkmaz, S., Goksulukm, D., & Zararsiz, G. (2014). MVN: An R Package for Assessing Multivariate Normality. The R Journal, 6(2), 151–162. https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdfCrossRef

Lee, C. H. J., & Sibley, C. G. (2020). Attitudes toward vaccinations are becoming more polarized in New Zealand: Findings from a longitudinal survey. EClinicalMedicine, 23, 100387. https://doi.org/10.1016/j.eclinm.2020.100387CrossRefPubMedPubMedCentral

Li, Y., Tenchov, R., Smoot, J., Liu, C., Watkins, S., & Zhou, Q. (2021). A comprehensive review of the global efforts on COVID-19 vaccine development. ACS Central Science, 7(4), 512–533. https://doi.org/10.1021/acscentsci.1c00120CrossRefPubMedPubMedCentral

Little, T. D. (2013). Longitudinal structural equation modeling. Guilford Press.

Mackinnon, D. P. (2012). Introduction to statistical mediation analysis. Routledge. https://doi.org/10.4324/9780203809556CrossRef

Maintainer, O., & Osorio, F. (2015). Title estimation and testing for the multivariate t-distribution.

Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320–341. https://doi.org/10.1207/s15328007sem1103_2CrossRef

McBride, O., Murphy, J., Shevlin, M., Gibson-Miller, J., Hartman, T. K., Hyland, P., Levita, L., Mason, L., Martinez, A. P., McKay, R., Stocks, T. V. A., Bennett, K. M., Vallières, F., Karatzias, T., Valiente, C., Vazquez, C., & Bentall, R. P. (2021). Monitoring the psychological, social, and economic impact of the COVID-19 pandemic in the population: Context, design and conduct of the longitudinal COVID-19 psychological research consortium (C19PRC) study. International Journal of Methods in Psychiatric Research, 30, 1–55. https://doi.org/10.1002/mpr.1861CrossRef

Murphy, J., Vallières, F., Bentall, R. P., Shevlin, M., McBride, O., Hartman, T. K., McKay, R., Bennett, K., Mason, L., Gibson-Miller, J., Levita, L., Martinez, A. P., Stocks, T. V. A., Karatzias, T., & Hyland, P. (2021). Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom. Nature Communications. https://doi.org/10.1038/s41467-020-20226-9CrossRefPubMedPubMedCentral

Panzeri, A., Bertamini, M., Butter, S., Levita, L., Gibson-Miller, J., Vidotto, G., Bentall, R. P., & Bennett, K. M. (2021). Factors impacting resilience as a result of exposure to COVID-19: The ecological resilience model. PLoS ONE, 16, e0256041. https://doi.org/10.1371/journal.pone.0256041CrossRefPubMedPubMedCentral

Panzeri, A., & Rossi Ferrario, S. (2020). Supporting rehabilitation patients with COVID-19 during the pandemic: Experiences from a technology-based psychological approach. CEUR Workshop Proceedings: Second Symposium on Psychology-Based Technologies—Psychobit, 2730.

Panzeri, A., Rossi Ferrario, S., & Cerutti, P. (2021). Psychological differences among healthcare workers of a rehabilitation institute during the COVID-19 pandemic: A two-step study. Frontiers in Psychology, 12, 1–11. https://doi.org/10.3389/fpsyg.2021.636129CrossRef

Parola, A., Rossi, A., Tessitore, F., Troisi, G., & Mannarini, S. (2020). Mental health through the COVID-19 quarantine: A growth curve analysis on Italian young adults. Frontiers in Psychology, 11, 2466. https://doi.org/10.3389/fpsyg.2020.567484CrossRef

Pastore, M., & Altoè, G. (2018). Package “influence.SEM” Type Package Title Case Influence in Structural Equation Models. http://lavaan.ugent.be/tutorial/tutorial

Pivetti, M., Melotti, G., Bonomo, M., & Hakoköngäs, E. (2021). Conspiracy beliefs and acceptance of COVID-vaccine: An exploratory study in Italy. Social Sciences. https://doi.org/10.3390/socsci10030108CrossRef

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. https://doi.org/10.1037/0021-9010.88.5.879CrossRefPubMed

R Core Team. (2021). R: A language and environment for statistical computing. In R Foundation for Statistical Computing, Vienna, Austria. R Foundation for Statistical Computing. https://www.r-project.org/

Romer, D., & Jamieson, K. H. (2020). Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Social Science & Medicine, 263, 113356. https://doi.org/10.1016/J.SOCSCIMED.2020.113356CrossRef

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36. https://doi.org/10.18637/jss.v048.i02CrossRef

Rossi, A., Panzeri, A., Pietrabissa, G., Manzoni, G. M., Castelnuovo, G., Mannarini, S., & Mannarini, S. (2020). The anxiety-buffer hypothesis in the time of COVID-19: When self-esteem protects from the impact of loneliness and fear on anxiety and depression. Frontiers in Psychology, 11, 2177. https://doi.org/10.3389/fpsyg.2020.02177CrossRefPubMedPubMedCentral

Rossi, A. A., Marconi, M., Taccini, F., Verusio, C., & Mannarini, S. (2021). From fear to hopelessness: The buffering effect of patient-centered communication in a sample of oncological patients during covid-19. Behavioral Sciences, 11, 87. https://doi.org/10.3390/bs11060087CrossRefPubMedPubMedCentral

Rossi Ferrario, S., Panzeri, A., Cerutti, P., & Sacco, D. (2021). The psychological experience and intervention in post-acute COVID-19 inpatients. Neuropsychiatric Disease and Treatment, 17, 413–422. https://doi.org/10.2147/NDT.S283558CrossRefPubMedPubMedCentral

Sallam, M. (2021). COVID-19 vaccine hesitancy worldwide: A concise systematic review of vaccine acceptance rates. Vaccines. https://doi.org/10.3390/vaccines9020160CrossRefPubMedPubMedCentral

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514. https://doi.org/10.1007/BF02296192CrossRef

Sturgis, P., Brunton-Smith, I., & Jackson, J. (2021). Trust in science, social consensus and vaccine confidence. Nature Human Behaviour. https://doi.org/10.1038/s41562-021-01115-7CrossRefPubMed

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (7th ed.). USA: Pearson.

WHO. (2021). Weekly epidemiological update on COVID-19—6 July 2021. Emergency Situational Updates. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---17-august-2021

Widaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8, 16–37. https://doi.org/10.1037/1082-989X.8.1.16CrossRefPubMed

Title: The mediating role of scientifical-medical satisfaction between COVID-19 conspiracy beliefs and vaccine confidence: a two-waves structural equation model
Authors: Giuseppe Mignemi
Anna Panzeri
Umberto Granziol
Giovanni Bruno
Marco Bertamini
Giulio Vidotto
Andrea Spoto
Publication date: 22-06-2022
Publisher: Springer US
Keyword: COVID-19
Published in: Journal of Behavioral Medicine / Issue 1-2/2023
Print ISSN: 0160-7715
Electronic ISSN: 1573-3521
DOI: https://doi.org/10.1007/s10865-022-00322-5

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The mediating role of scientifical-medical satisfaction between COVID-19 conspiracy beliefs and vaccine confidence: a two-waves structural equation model

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