Evidence map›Paper›PMID 33705439›Full record

ArticlePloS one2021

Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study.

Stephanie Josephine Eder, David Steyrl, Michal Mikolaj Stefanczyk, Michał Pieniak, Judit Martínez Molina, Ondra Pešout, Jakub Binter, Patrick Smela, Frank Scharnowski, Andrew A Nicholson

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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  11. Trust in Healthcare during COVID-19 in Europe: vulnerable groups trust the least.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2022
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  12. Pharmacological Adherence Behavior Changes during COVID-19 Outbreak in a Portugal Patient Cohort.International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Stephanie Josephine EderDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.ORCID 0000-0002-2061-5382
David SteyrlDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
Michal Mikolaj StefanczykInstitute of Psychology, University of Wroclaw, Wroclaw, Poland.
Michał PieniakInstitute of Psychology, University of Wroclaw, Wroclaw, Poland.
Judit Martínez MolinaFaculty of Psychology, University of Barcelona, Barcelona, Spain.
Ondra PešoutDepartment of Psychology, Jan Evangelista Purkyne University, Ústí nad Labem, Czech Republic.
Jakub BinterDepartment of Anthropology, Faculty of Humanities, Charles University, Prague, Czech Republic.
Patrick SmelaDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
Frank ScharnowskiDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
Andrew A NicholsonDepartment of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

FearAdultAnxietyAttitude to HealthCOVID-19FemaleHealth StatusHumansLongitudinal StudiesMachine LearningMaleSelf ReportSocial IsolationYoung Adult

Identifiers

PMID33705439
PMCPMC7951840

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.