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

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