Contextual factors predicting compliance behavior during the COVID-19 pandemic : a machine learning analysis on survey data from 16 countries

2022
journal article
article
dc.abstract.enVoluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country’s sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.pl
dc.affiliationSzkoła Doktorska Nauk Społecznychpl
dc.affiliationWydział Filozoficzny : Instytut Psychologiipl
dc.contributor.authorHajdu, Nandorpl
dc.contributor.authorSchmidt, Kathleenpl
dc.contributor.authorAcs, Gergelypl
dc.contributor.authorRöer, Jan P.pl
dc.contributor.authorMirisola, Albertopl
dc.contributor.authorGiammusso, Isabellapl
dc.contributor.authorArriaga, Patríciapl
dc.contributor.authorRibeiro, Rafaelpl
dc.contributor.authorDubrov, Dmitriipl
dc.contributor.authorGrigoryev, Dmitrypl
dc.contributor.authorArinze, Nwadiogo C.pl
dc.contributor.authorVoracek, Martinpl
dc.contributor.authorStieger, Stefanpl
dc.contributor.authorAdamkovic, Matuspl
dc.contributor.authorElsherif, Mahmoudpl
dc.contributor.authorKern, Bettina M. J.pl
dc.contributor.authorBarzykowski, Krystian - 105054 pl
dc.contributor.authorIlczuk, Ewa - 371618 pl
dc.contributor.authorMartončik, Marcelpl
dc.contributor.authorRopovik, Ivanpl
dc.contributor.authorRuiz-Fernandez, Susanapl
dc.contributor.authorBaník, Gabrielpl
dc.contributor.authorUlloa, José Luispl
dc.contributor.authorAczel, Balazspl
dc.contributor.authorSzaszi, Barnabaspl
dc.date.accessioned2023-01-09T09:33:38Z
dc.date.available2023-01-09T09:33:38Z
dc.date.issued2022pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.number11pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume17pl
dc.identifier.articleide0276970pl
dc.identifier.doi10.1371/journal.pone.0276970pl
dc.identifier.eissn1932-6203pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/305750
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa*
dc.rights.licenceCC-BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode.pl*
dc.share.typeotwarte czasopismo
dc.subtypeArticlepl
dc.titleContextual factors predicting compliance behavior during the COVID-19 pandemic : a machine learning analysis on survey data from 16 countriespl
dc.title.journalPLoS ONEpl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.abstract.enpl
Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country’s sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.
dc.affiliationpl
Szkoła Doktorska Nauk Społecznych
dc.affiliationpl
Wydział Filozoficzny : Instytut Psychologii
dc.contributor.authorpl
Hajdu, Nandor
dc.contributor.authorpl
Schmidt, Kathleen
dc.contributor.authorpl
Acs, Gergely
dc.contributor.authorpl
Röer, Jan P.
dc.contributor.authorpl
Mirisola, Alberto
dc.contributor.authorpl
Giammusso, Isabella
dc.contributor.authorpl
Arriaga, Patrícia
dc.contributor.authorpl
Ribeiro, Rafael
dc.contributor.authorpl
Dubrov, Dmitrii
dc.contributor.authorpl
Grigoryev, Dmitry
dc.contributor.authorpl
Arinze, Nwadiogo C.
dc.contributor.authorpl
Voracek, Martin
dc.contributor.authorpl
Stieger, Stefan
dc.contributor.authorpl
Adamkovic, Matus
dc.contributor.authorpl
Elsherif, Mahmoud
dc.contributor.authorpl
Kern, Bettina M. J.
dc.contributor.authorpl
Barzykowski, Krystian - 105054
dc.contributor.authorpl
Ilczuk, Ewa - 371618
dc.contributor.authorpl
Martončik, Marcel
dc.contributor.authorpl
Ropovik, Ivan
dc.contributor.authorpl
Ruiz-Fernandez, Susana
dc.contributor.authorpl
Baník, Gabriel
dc.contributor.authorpl
Ulloa, José Luis
dc.contributor.authorpl
Aczel, Balazs
dc.contributor.authorpl
Szaszi, Barnabas
dc.date.accessioned
2023-01-09T09:33:38Z
dc.date.available
2023-01-09T09:33:38Z
dc.date.issuedpl
2022
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.numberpl
11
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
17
dc.identifier.articleidpl
e0276970
dc.identifier.doipl
10.1371/journal.pone.0276970
dc.identifier.eissnpl
1932-6203
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/305750
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa
dc.rights.licence
CC-BY
dc.rights.uri*
http://creativecommons.org/licenses/by/4.0/legalcode.pl
dc.share.type
otwarte czasopismo
dc.subtypepl
Article
dc.titlepl
Contextual factors predicting compliance behavior during the COVID-19 pandemic : a machine learning analysis on survey data from 16 countries
dc.title.journalpl
PLoS ONE
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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