During pandemics, protective behaviors must be motivated by effective communication. A critical factor in understanding a population’s response to such a threat is the fear it elicits, as fear both helps motivate protective responses, but can also lead to panic behaviors. Additionally, lockdown measures affect well-being, making it important to identify protective factors that help keep perceived levels of health high during restrictions. An international team of researchers led by scientists from the University of Vienna has now identified psychological predictors of fear and health during shutdowns. The result of the study, published in PLOS ONE: Individual psychological variables have a much better predictive power than environmental variables.
The current publication aims to identify predictors of fear and perceived health during stay-at-home orders in response to COVID-19[female[feminine pandemic. “In this way, we can predict how different people and populations will react to external threats and restrictions,” explains Stephanie Eder of the Faculty of Psychology.
Researchers from the University of Vienna in collaboration with scientists from the University of Wroclaw (PL), University of Barcelona (ESP), Charles University and Jan Evangelista Purkyne University (CZ) have surveyed 533 participants during the “first wave” of the COVID-19 pandemic in Europe.
Using machine learning models, they identified psychological predictors of fear and health during blockages. Fear can be predicted very well when concerns about supply shortages, perceived infectability of diseases in general, aversion to germs, and infections in the immediate social sphere are taken into account. Predictors of perceived health include higher perceived infectability to disease in general, attachment security, physical activity and young age; suggesting that older populations with high perceived infectability and insecure attachment may be the most vulnerable in these uncertain times.
Interestingly, environmental variables such as local severity of lockdown restrictions and mortality had no predictive value for either of the target variables. “We could show the value of psychological factors at the micro level versus large-scale environmental conditions when predicting a population’s response to a crisis and when designing behavioral interventions for specific target groups,” explains Eder.
Reference: “Predicting Fear and Perceived Health During the COVID-19 Pandemic Using Machine Learning: A Cross-National Longitudinal Study” by Stephanie Josephine Eder, David Steyrl, Michal Mikolaj Stefanczyk, Michał Pieniak, Judit Martínez Molina , Ondra Pešout, Jakub Binter, Patrick Smela, Frank Scharnowski and Andrew A. Nicholson, March 11, 2021, PLOS ONE.
DOI: 10.1371 / journal.pone.0247997