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

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posted on 2023-08-03, 14:00 authored by N Hajdu, K Schmidt, G Acs, JP Röer, A Mirisola, I Giammusso, P Arriaga, R Ribeiro, D Dubrov, D Grigoryev, NC Arinze, M Voracek, S Stieger, M Adamkovic, M Elsherif, BMJ Kern, K Barzykowski, E Ilczuk, M Martončik, I Ropovik, S Ruiz-Fernandez, G Baník, JL Ulloa, B Aczel, B Szaszi
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.

Funding

NKFIH-1157-8/2019-DT

Posttraumatic subtype of depression and its etiopathogenesis: network approach to psychopathology

Slovak Research and Development Agency

Find out more...

UID/PSI/03125/2020

Behavioural aspects of COVID-19: Mapping the COVID-related behaviours and psychological, social, and economic consequences of the pandemic

Slovak Research and Development Agency

Find out more...

UMO-2019/35/B/HS6/00528

TKP2021

History

Citation

Hajdu N, Schmidt K, Acs G, Röer JP, Mirisola A, Giammusso I, et al. (2022) Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries. PLoS ONE 17(11): e0276970. https://doi.org/10.1371/journal.pone.0276970

Author affiliation

School of Psychology and Vision Sciences, University of Leicester

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  • VoR (Version of Record)

Published in

PLoS ONE

Volume

17

Issue

11 November

Pagination

e0276970

Publisher

Public Library of Science (PLoS)

issn

1932-6203

eissn

1932-6203

Acceptance date

2022-10-18

Copyright date

2022

Available date

2023-08-03

Spatial coverage

United States

Language

en

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