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; +95 more... 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 BI; 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; (2022) Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns, 3 (4). 100482-. ISSN 2666-3899 DOI: https://doi.org/10.1016/j.patter.2022.100482
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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.
Item Type | Article |
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Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Research Centre | Covid-19 Research |
PubMed ID | 35282654 |
Elements ID | 176335 |
Official URL | http://dx.doi.org/10.1016/j.patter.2022.100482 |