Abstract
Before vaccines for coronavirus disease 2019 (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 to 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.
•We studied predictors of COVID-19 prevention behaviors in a cross-national study•The strongest predictors related to injunctive norms
In the absence of a vaccine or cure, virus containment depended on individual-level compliance with behaviors recommended by the World Health Organization. We used machine learning to identify the most important indicators of compliance, based on a large international psychological survey and on country-level secondary data. The most important indicators were not the “usual suspects,” such as personal threat of virus infection, but rather injunctive norms—namely, the belief that one’s community should engage in such behavior and that society should take restrictive virus-containment measures. People who tend to engage in infection-prevention behaviors also tend to believe that general compliance is necessary to defeat the pandemic, which extends to endorsement of “ought” norms and support for behavioral mandates. These results highlight the potential to intervene by shaping social norms and expectations.
In a study of 56,072 participants from 28 countries, we used a machine-learning approach to identify the strongest predictors of COVID-19-infection-prevention behavior (pre-vaccine). Few country-level data variables predicted outcomes. Instead, individual psychological variables predicted outcomes. Injunctive norms such as believing people should engage in the behaviors and support for behavioral mandates were the strongest predictors of infection-prevention behavior. The results highlight how both data- and theory-driven approaches can increase understanding of complex human behavior.
