Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
dc.contributor.author | van Lissa, Caspar J. | |
dc.contributor.author | Stroebe, Wolfgang | |
dc.contributor.author | vanDellen, Michelle R. | |
dc.contributor.author | Leander, N. Pontus | |
dc.contributor.author | Agostini, Maximilian | |
dc.contributor.author | Draws, Tim | |
dc.contributor.author | Grygoryshyn, Andrii | |
dc.contributor.author | Gützgow, Ben | |
dc.contributor.author | Kreienkamp, Jannis | |
dc.contributor.author | Vetter, Clara S. | |
dc.contributor.author | Abakoumkin, Georgios | |
dc.contributor.author | Abdul Khaiyom, Jamilah Hanum | |
dc.contributor.author | Ahmedi, Vjolica | |
dc.contributor.author | Akkas, Handan | |
dc.contributor.author | Almenara, Carlos A. | |
dc.contributor.author | Atta, Mohsin | |
dc.contributor.author | Bagci, Sabahat Cigdem | |
dc.contributor.author | Basel, Sima | |
dc.contributor.author | Kida, Edona Berisha | |
dc.contributor.author | Bernardo, Allan B.I. | |
dc.contributor.author | Buttrick, Nicholas R. | |
dc.contributor.author | Chobthamkit, Phatthanakit | |
dc.contributor.author | Choi, Hoon Seok | |
dc.contributor.author | Cristea, Mioara | |
dc.contributor.author | Csaba, Sára | |
dc.contributor.author | Damnjanović, Kaja | |
dc.contributor.author | Danyliuk, Ivan | |
dc.contributor.author | Dash, Arobindu | |
dc.contributor.author | Di Santo, Daniela | |
dc.contributor.author | Douglas, Karen M. | |
dc.contributor.author | Enea, Violeta | |
dc.contributor.author | Faller, Daiane Gracieli | |
dc.contributor.author | Fitzsimons, Gavan J. | |
dc.contributor.author | Gheorghiu, Alexandra | |
dc.contributor.author | Gómez, Ángel | |
dc.contributor.author | Hamaidia, Ali | |
dc.contributor.author | Han, Qing | |
dc.contributor.author | Helmy, Mai | |
dc.contributor.author | Hudiyana, Joevarian | |
dc.contributor.author | Jeronimus, Bertus F. | |
dc.contributor.author | Jiang, Ding Yu | |
dc.contributor.author | Jovanović, Veljko | |
dc.contributor.author | Kamenov, Željka | |
dc.contributor.author | Kende, Anna | |
dc.contributor.author | Keng, Shian Ling | |
dc.contributor.author | Thanh Kieu, Tra Thi | |
dc.contributor.author | Koc, Yasin | |
dc.contributor.author | Kovyazina, Kamila | |
dc.contributor.author | Kozytska, Inna | |
dc.contributor.author | Krause, Joshua | |
dc.contributor.author | Kruglanksi, Arie W. | |
dc.contributor.author | Kurapov, Anton | |
dc.contributor.author | Kutlaca, Maja | |
dc.contributor.author | Lantos, Nóra Anna | |
dc.contributor.author | Lemay, Edward P. | |
dc.contributor.author | Jaya Lesmana, Cokorda Bagus | |
dc.contributor.author | Louis, Winnifred R. | |
dc.contributor.author | Lueders, Adrian | |
dc.contributor.author | Malik, Najma Iqbal | |
dc.contributor.author | Martinez, Anton P. | |
dc.contributor.author | McCabe, Kira O. | |
dc.contributor.author | Mehulić, Jasmina | |
dc.contributor.author | Milla, Mirra Noor | |
dc.contributor.author | Mohammed, Idris | |
dc.contributor.author | Molinario, Erica | |
dc.contributor.author | Moyano, Manuel | |
dc.contributor.author | Muhammad, Hayat | |
dc.contributor.author | Mula, Silvana | |
dc.contributor.author | Muluk, Hamdi | |
dc.contributor.author | Myroniuk, Solomiia | |
dc.contributor.author | Najafi, Reza | |
dc.contributor.author | Nisa, Claudia F. | |
dc.contributor.author | Nyúl, Boglárka | |
dc.contributor.author | O'Keefe, Paul A. | |
dc.contributor.author | Olivas Osuna, Jose Javier | |
dc.contributor.author | Osin, Evgeny N. | |
dc.contributor.author | Park, Joonha | |
dc.contributor.author | Pica, Gennaro | |
dc.contributor.author | Pierro, Antonio | |
dc.contributor.author | Rees, Jonas H. | |
dc.contributor.author | Reitsema, Anne Margit | |
dc.contributor.author | Resta, Elena | |
dc.contributor.author | Rullo, Marika | |
dc.contributor.author | Ryan, Michelle K. | |
dc.contributor.author | Samekin, Adil | |
dc.contributor.author | Santtila, Pekka | |
dc.contributor.author | Sasin, Edyta M. | |
dc.contributor.author | Schumpe, Birga M. | |
dc.contributor.author | Selim, Heyla A. | |
dc.contributor.author | Stanton, Michael Vicente | |
dc.contributor.author | Sultana, Samiah | |
dc.contributor.author | Sutton, Robbie M. | |
dc.contributor.author | Tseliou, Eleftheria | |
dc.contributor.author | Utsugi, Akira | |
dc.contributor.author | Anne van Breen, Jolien | |
dc.contributor.author | van Veen, Kees | |
dc.contributor.author | Vázquez, Alexandra | |
dc.contributor.author | Wollast, Robin | |
dc.contributor.author | Wai-Lan Yeung, Victoria | |
dc.contributor.author | Zand, Somayeh | |
dc.date.accessioned | 2022-05-06T15:50:47Z | |
dc.date.available | 2022-05-06T15:50:47Z | |
dc.date.issued | 2022-04-08 | |
dc.identifier.doi | 10.1016/j.patter.2022.100482 | |
dc.identifier.uri | http://hdl.handle.net/10757/659813 | |
dc.description.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. | es_PE |
dc.description.sponsorship | New York University Abu Dhabi | es_PE |
dc.format | application/pdf | es_PE |
dc.language.iso | eng | es_PE |
dc.publisher | Cell Press | es_PE |
dc.relation.url | https://www.cell.com/patterns/fulltext/S2666-3899(22)00067-8 | es_PE |
dc.rights | info:eu-repo/semantics/openAccess | es_PE |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.source | Universidad Peruana de Ciencias Aplicadas (UPC) | es_PE |
dc.source | Repositorio Academico - UPC | es_PE |
dc.subject | COVID-19 | es_PE |
dc.subject | Health behaviors | es_PE |
dc.subject | Machine learning | es_PE |
dc.subject | Public goods dilemma | es_PE |
dc.subject | Random forest | es_PE |
dc.subject | Social norms | es_PE |
dc.title | Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic | es_PE |
dc.type | info:eu-repo/semantics/article | es_PE |
dc.identifier.eissn | 26663899 | |
dc.identifier.journal | Patterns | es_PE |
dc.description.peerreview | Revisión por pares | es_PE |
dc.identifier.eid | 2-s2.0-85127500709 | |
dc.identifier.scopusid | SCOPUS_ID:85127500709 | |
dc.identifier.pii | S2666389922000678 | |
dc.source.journaltitle | Patterns | |
dc.source.volume | 3 | |
dc.source.issue | 4 | |
refterms.dateFOA | 2022-05-06T15:50:48Z | |
dc.identifier.isni | 0000 0001 2196 144X |
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