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  • Using machine learning to unveil relevant predictors of adherence to recommended health‐protective behaviors during the COVID‐19 pandemic in Denmark
Title: Using machine learning to unveil relevant predictors of adherence to recommended health‐protective behaviors during the COVID‐19 pandemic in Denmark
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.  more » « less
Award ID(s):
2229881
PAR ID:
10699338
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
Applied Psychology: Health and Well‐Being
Date Published:
Journal Name:
Applied Psychology: Health and Well-Being
Volume:
16
Issue:
4
ISSN:
1758-0846
Page Range / eLocation ID:
1819 to 1839
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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