Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13430%2F21%3A43896123" target="_blank" >RIV/44555601:13430/21:43896123 - isvavai.cz</a>
Výsledek na webu
<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247997" target="_blank" >https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247997</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1371/journal.pone.0247997" target="_blank" >10.1371/journal.pone.0247997</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study
Popis výsledku v původním jazyce
During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of "macro-level" environmental factors and "micro-level" psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of "micro-level" psychological factors, as opposed to "macro-level" environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health.
Název v anglickém jazyce
Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study
Popis výsledku anglicky
During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of "macro-level" environmental factors and "micro-level" psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of "micro-level" psychological factors, as opposed to "macro-level" environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50101 - Psychology (including human - machine relations)
Návaznosti výsledku
Projekt
—
Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
PLOS ONE
ISSN
1932-6203
e-ISSN
—
Svazek periodika
16
Číslo periodika v rámci svazku
3
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
16
Strana od-do
1-16
Kód UT WoS článku
000629590400017
EID výsledku v databázi Scopus
—