Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark

Lau Lilleholt (Korresp. Autor*in), Gretchen B Chapman, Robert Böhm, Ingo Zettler

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

Abstract

What were relevant predictors of individuals' proclivity to adhere to recommended health-protective behaviors during the COVID-19 pandemic in Denmark? Applying machine learning (namely, lasso regression) to a repeated cross-sectional survey spanning 10 months comprising 25 variables (Study 1; N = 15,062), we found empathy toward those most vulnerable to COVID-19, knowledge about how to protect oneself from getting infected, and perceived moral costs of nonadherence to be strong predictors of individuals' self-reported adherence to recommended health-protective behaviors. We further explored the relations between these three factors and individuals' self-reported proclivity for adherence to recommended health-protective behaviors as they unfold between and within individuals over time in a second study, a Danish panel study comprising eight measurement occasions spanning eight months (N = 441). Results of this study suggest that the relations largely occurred at the trait-like interindividual level, as opposed to at the state-like intraindividual level. Together, the findings provide insights into what were relevant predictors for individuals' overall level of adherence to recommended health-protective behaviors (in Denmark) as well as how these predictors might (not) be leveraged to promote public adherence in future epidemics or pandemics.

OriginalspracheEnglisch
Seiten (von - bis)1819-1839
Seitenumfang21
FachzeitschriftApplied Psychology: Health and Well-Being
Jahrgang16
Ausgabenummer4
Frühes Online-Datum8 Juni 2024
DOIs
PublikationsstatusVeröffentlicht - Nov. 2024

ÖFOS 2012

  • 501021 Sozialpsychologie

Fingerprint

Untersuchen Sie die Forschungsthemen von „Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitationsweisen