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Identifying predictors of vaccination willingness and attitudes during Covid-19: Machine learning multi-country study

Published online by Cambridge University Press:  19 July 2023

M. Makhubela
M. Makhubela*
Affiliation:
University of Limpopo, Polokwane, South Africa

Abstract

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Introduction

While there is some research that shows personal and psychological factors to be linked to disease-avoidant behaviour and attitudes in the time of Covid-19, this research is however mixed and inconsistent (i.e., some studies report a link and others do not).

Objectives

In this study we clarify whether demographic and psychological factors specifically predict vaccination willingness and attitudes using Machine learning of a global survey sample from 137 countries (N = 24 000).

Methods

Random forest machine learning algorithm was used to identify the strongest predictors of vaccination willingness and attitudes, while regression trees were developed to identify individuals at greater risk for anti-vaccination attitudes.

Results

Conspiratorial thinking and lack of trust in experts were associated with vaccination attitudes and willingness.

Conclusions

The findings underscore the role of conspiratorial beliefs in shaping the uptake of non-pharmacological and pharmacological novel pandemic protective measures.

Disclosure of Interest

None Declared

Type
Abstract
Information
European Psychiatry , Volume 66 , Special Issue S1: Abstracts of the 31st European Congress of Psychiatry , March 2023 , pp. S410
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the European Psychiatric Association
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