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Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Van Lissa, Caspar J., Stroebe, Wolfgang, vanDellen, Michelle R., Leander, Pontus, Agostini, Maximilian, Draws, T, Grygoryshyn, A, Gützkow, Ben, Kreienkamp, Jannis, Vetter, C, and others. (2022) Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns, . E-ISSN 2666-3899. (doi:10.1016/j.patter.2022.100482) (KAR id:93618)

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https://doi.org/10.1016/j.patter.2022.100482

Abstract

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant.

Item Type: Article
DOI/Identification number: 10.1016/j.patter.2022.100482
Uncontrolled keywords: Machine learning, COVID-19, Health Behaviors, Social Norms, Public Goods Dilemma
Subjects: Q Science
Divisions: Divisions > Division of Human and Social Sciences > School of Psychology
Depositing User: Karen Douglas
Date Deposited: 16 Mar 2022 13:23 UTC
Last Modified: 17 Mar 2022 14:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93618 (The current URI for this page, for reference purposes)
Douglas, Karen: https://orcid.org/0000-0002-0381-6924
Sutton, Robbie M.: https://orcid.org/0000-0003-1542-1716
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