Open AccessKNOWLEDGE, ATTITUDES AND COVID-19-RELATED BEHAVIOR AMONG INDIVIDUALS AGED 50 AND OLDER IN LATVIA
Author(s) -
Ilona Gehtmane-Hofmane,
Madara Teraudkalna,
Olga Rajevska,
Ilze Koroļeva,
Ieva Reine
Publication year - 2021
Publication title -
sabiedrība, integrācija, izglītība/sabiedrība. integrācija. izglītība/society. integration. education
Language(s) - English
Resource type - Journals
eISSN - 2256-0629
pISSN - 1691-5887
DOI - 10.17770/sie2021vol4.6462
Subject(s) - pandemic , covid-19 , population , outbreak , environmental health , qualitative research , gerontology , medicine , psychology , sample (material) , disease , infectious disease (medical specialty) , sociology , virology , social science , chemistry , pathology , chromatography
The coronavirus disease 2019 (COVID-19) was declared a pandemic by the WHO on March 11, 2020, due to its high infection rate, which caused thousands of deaths worldwide and expanding. The evolving outbreak of COVID-19 requires health-protective behavior that can alleviate the severity of an epidemic. Therefore, recognizing the underlying drivers of health-protective behavior against COVID-19 is urgently needed to form policy responses. The purpose of this study was to investigate the individual-level underlying drivers affecting the formation of knowledge, attitudes, and COVID-19-related health-protective behavior among individuals aged 50 and older who are more vulnerable to complications of infection because of their age. The factors making individuals more vulnerable are also associated with the ability to access and understand information, make well-informed decisions, and take health-protective and promoting actions, especially when information itself is not timely, trusted, consistent, or actionable. The researchers used data from a representative population sample consisting of 50-80-year-old individuals, obtained from in-depth, semi-structured telephone interviews during July – August 2020, between the first and second waves of COVID-19. For conducting qualitative data analyses, the Grounded Theory (GT) approach for developing code structure was used.