Van Lissa, CJ; Stroebe, W; vanDellen, MR; Leander, NP; Agostini, M; Draws, T; Grygoryshyn, A; Gützgow, B; Kreienkamp, J; Vetter, CS; Abakoumkin, G; Abdul Khaiyom, JH; Ahmedi, V; Akkas, H; Almenara, CA; Atta, M; Bagci, SC; Basel, S; Kida, EB; Bernardo, ABI; Buttrick, NR; Chobthamkit, P; Choi, H-S; Cristea, M; Csaba, S; Damnjanović, K; Danyliuk, I; Dash, A; Di Santo, D; Douglas, KM; Enea, V; Faller, DG; Fitzsimons, GJ; Gheorghiu, A; Gómez, Á; Hamaidia, A; Han, Q; Helmy, M; Hudiyana, J; Jeronimus, BF; Jiang, D-Y; Jovanović, V; Kamenov, Ž; Kende, A; Keng, S-L; Thanh Kieu, TT; Koc, Y; Kovyazina, K; Kozytska, I; Krause, J; Kruglanksi, AW; Kurapov, A; Kutlaca, M; Lantos, NA; Lemay, EP; Jaya Lesmana, CB; Louis, WR; Lueders, A; Malik, NI; Martinez, AP; McCabe, KO; Mehulić, J; Milla, MN; Mohammed, I; Molinario, E; Moyano, M; Muhammad, H; Mula, S; Muluk, H; Myroniuk, S; Najafi, R; Nisa, CF; Nyúl, B; O'Keefe, PA; Olivas Osuna, JJ; Osin, EN; Park, J; Pica, G; Pierro, A; Rees, JH; Reitsema, AM; Resta, E; Rullo, M; Ryan, MK; Samekin, A; Santtila, P; Sasin, EM; Schumpe, BM; Selim, HA; Stanton, MV; Sultana, S; Sutton, RM; Tseliou, E; Utsugi, A; Anne van Breen, J; Van Veen, K; Vázquez, A; Wollast, R; Wai-Lan Yeung, V; Zand, S; Žeželj, IL; Zheng, B; Zick, A; Zúñiga, C; Bélanger, JJ (2022). Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.. Patterns (New York, N.Y.), 3(4), 100482.
(last updated on 2024/04/23)
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.