Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic (CROSBI ID 320294)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Van Lissa, Caspar J. ; Stroebe, Wolfgang ; vanDellen, Michelle R. ; Leander, N. Pontus ; Agostini, Maximilian ; Draws, Tim ; Grygoryshyn, Andrii ; Gützgow, Ben ; Kreienkamp, Jannis ; Vetter, Clara S. ; Abakoumkin, Georgios ; Abdul Khaiyom, Jamilah Hanum ; Ahmedi, Vjolica ; Akkas, Handan ; Almenara, Carlos A. ; Atta, Mohsin ; Bagci, Sabahat Cigdem ; Basel, Sima ; Kida, Edona Berisha ; Bernardo, Allan B.I. ; Buttrick, Nicholas R. ; Chobthamkit, Phatthanakit ; Choi, Hoon-Seok ; Cristea, Mioara ; Csaba, Sára ; Damnjanović, Kaja ; Danyliuk, Ivan ; Dash, Arobindu ; Di Santo, Daniela ; Douglas, Karen M. ; Enea, Violeta ; Faller, Daiane Gracieli ; Fitzsimons, Gavan J. ; Gheorghiu, Alexandra ; Gómez, Ángel ; Hamaidia, Ali ; Han, Qing ; Helmy, Mai ; Hudiyana, Joevarian ; Jeronimus, Bertus F. ; Jiang, Ding-Yu ; Jovanović, Veljko ; Kamenov, Željka ; Kende, Anna ; Keng, Shian-Ling ; Thanh Kieu, Tra Thi ; Koc, Yasin ; Kovyazina, Kamila ; Kozytska, Inna ; Krause, Joshua ; Kruglanksi, Arie W. ; Kurapov, Anton ; Kutlaca, Maja ; Lantos, Nóra Anna ; Lemay, Edward P. ; Jaya Lesmana, Cokorda Bagus ; Louis, Winnifred R. ; Lueders, Adrian ; Malik, Najma Iqbal ; Martinez, Anton P. ; McCabe, Kira O. ; Mehulić, Jasmina ; Milla, Mirra Noor ; Mohammed, Idris ; Molinario, Erica ; Moyano, Manuel ; Muhammad, Hayat ; Mula, Silvana ; Muluk, Hamdi ; Myroniuk, Solomiia ; Najafi, Reza ; Nisa, Claudia F. ; Nyúl, Boglárka ; O’Keefe, Paul A. ; Olivas Osuna, Jose Javier ; Osin, Evgeny N. ; Park, Joonha ; Pica, Gennaro ; Pierro, Antonio ; Rees, Jonas H. ; Reitsema, Anne Margit ; Resta, Elena ; Rullo, Marika ; Ryan, Michelle K. ; Samekin, Adil ; Santtila, Pekka ; Sasin, Edyta M. ; Schumpe, Birga M. ; Selim, Heyla A. ; Stanton, Michael Vicente ; Sultana, Samiah ; Sutton, Robbie M. ; Tseliou, Eleftheria ; Utsugi, Akira ; Anne van Breen, Jolien ; Van Veen, Kees ; Vázquez, Alexandra ; Wollast, Robin ; Wai-Lan Yeung, Victoria ; Zand, Somayeh ; Žeželj, Iris Lav ; Zheng, Bang ; Zick, Andreas ; Zúñiga, Claudia ; Bélanger, Jocelyn J.
PsyCorona Collaboration
engleski
Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
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.
Machine learning; COVID-19; Health Behaviors; Social Norms; Public Goods Dilemma; Random forest;
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
Povezanost rada
Psihologija
