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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 &quot;macro-level&quot; environmental factors and &quot;micro-level&quot; 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 &quot;micro-level&quot; psychological factors, as opposed to &quot;macro-level&quot; 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 &quot;macro-level&quot; environmental factors and &quot;micro-level&quot; 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 &quot;micro-level&quot; psychological factors, as opposed to &quot;macro-level&quot; 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