@article{van_Lissa_C-2022_66739,
title = {Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic},
author = {van Lissa, C. and Stroebe, W. and vanDellen, M. and Leander, N. and Agostini, M. and Draws, T. and Grygoryshyn, A. and Gützkow, B. and Kreienkamp, J. and Vetter, C. and Abakoumkin, G. and Abdul Khaiyom, J. and Ahmedi, V. and Akkas, H. and Almenara, C. and Atta, M. and Bagci, S. and Basel, S. and Berisha Kida, E. and Bernardo, A. and Buttrick, N. and Chobthamkit, P. and Choi, H. and Cristea, M. and Csaba, S. and Damnjanović, K. and Danyliuk, I. and Dash, A. and Di Santo, D. and Douglas, K. and Enea, V. and Faller, D. and Fitzsimons, G. and Gheorghi, A. and Gómez, Á. and Grzymala‑Moszczynska, J. and Hamaidia, A. and Han, Q. and Helmy, M. and Hudiyana, J. and Jeronimus, B. and Jiang, D. and Jovanović, V. and Kamenov, Ž. and Kende, A. and Keng, S. and Kieu, T. and Koc, Y. and Kovyazina, K. and Kozytska, I. and Krause, J. and Kruglanski, A. and Kurapov, A. and Kutlaca, M. and Lantos, N. and Lemay, E. and Lemsmana, C. and Louis, W. and Lueders, A. and Malik, N. and Martinez, A. and McCabe, K. and Mehulić, J. and Milla, M. and Mohammed, I. and Molinario, E. and Moyano, M. and Muhammad, H. and Mula, S. and Muluk, H. and Myroniuk, S. and Najafi, R. and Nisa, C. and Nyúl, B. and O'Keefe, P. and Olivas Osuna, J. and Osin, E. and Park, J. and Pica, G. and Pierro, A. and Rees, J. and Reitsema, A. and Resta, E. and Rullo, M. and Ryan, M. and Samekin, A. and Santtila, P. and Sasin, E. and Schumpe, B. and Selim, H. and Stanton, M. and Sultana, S. and Sutton, R. and Tseliou, E. and Utsugi, A. and van Breen, J. and van Veen, K. and Vázquez, A. and Wollast, R. and Yeung, V. and Zand, S. and Žeželj, I. and Zheng, B. and Zick, A. and Zúñiga, C. and Bélanger, J.},
month = {mar},
year = {2022},
abstract = {In the early phase of the COVID-19 pandemic, before vaccines 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–information essential for guiding follow-up research and interventions. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates of health behaviors from a larger pool of candidate predictors. We used random forests, a machine learning algorithm, to rank 115 candidate correlates of infection prevention behavior in a study of 56,072 participants across 28 countries, administered in March-May 2020. Results indicated that the two most important predictors related to individual-level injunctive norms—beliefs that people in the community should engage in social distancing and self-isolation, followed by endorsement of restrictive containment measures. 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. 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 did not turn out to be important.},
volume = {3},
issue = {4},
journal = {Patterns},
publisher = {Elsevier},
url = {https://doi.org/10.1016/j.patter.2022.100482},
}
