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

Eder, Stephanie J.; Steyrl, David; Stefańczyk, Michał; Pieniak, Michał; Molina, Judit Martínez; Pešout, Ondra; Binter, Jakub; Smela, Patrick; Scharnowski, Frank; Nicholson, Andrew G.

doi:10.31234/osf.io/wdmvh

Cited by 9 publications

(12 citation statements)

References 31 publications

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“…Furthermore, secure attachment may relate to an internal locus of control (Hexel, 2003), and securely attached individuals may have more mechanisms to cope with the stress a world-wide crisis elicits (Feeney, 1995). Indeed, an analysis of subjective health in our sample over time shows that attachment security is a predictor of a heightened sense of health (Eder et al, 2020). This means that the effects of secure attachment are not refined to the relationship per se, but may exert an influence over various psychological mechanisms.…”

Section: Predictors Of Relationship Qualitymentioning

confidence: 82%

Securing your relationship: Quality of intimate relationships during the COVID-19 pandemic can be predicted by attachment style

Eder¹,

Nicholson²,

Stefańczyk³

et al. 2020

Preprint

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The COVID-19 pandemic presents a global medical stressor and brought about unprecedented governmental regulations. The present study investigates the romantic relationships of 350 participants from Austria, Poland, Spain and Czech Republic as movement restrictions were implemented and phased out in those countries. Participants filled in questionnaires over a period of seven weeks, and we predicted relationship using machine learning models that include a variety of potential predictors related to psychological, demographic and environmental factors.On average, our models predict 32 percent of the variance of relationship quality, the most important predictors being attachment security and amount of fights. We demonstrate that attachment security is a major predictor of relationship quality in these unstable times, whereas sexual behavior, fear, pathogenic threat and the severity of governmental regulations do not influence these variables. Statement of relevanceThe COVID-19 pandemic along with the curfews that were introduced in Europe in spring 2020 presents a unique opportunity to study the effects of environmental factors on intimate relationships and identify factors that contribute to relationship quality under stressful conditions. The present study took place precisely as strict measures in response to the rising spread of the virus were adopted in Austria, Poland, Spain and Czech Republic and ends as the main restrictions were dropped. We examine predictors of relationship quality and sexual behavior in these unstable times, and found that attachment security is one of the main predictors, whereas fear of the virus or environmental factors such as the stringency of curfews do not hold any predictive value.The cross-cultural composition of our sample and the fact that our models are evaluated by their performance on unknown subjects makes these findings relatively generalizable for western cultures.

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Section: Predictors Of Relationship Qualitymentioning

confidence: 82%

Securing your relationship: Quality of intimate relationships during the COVID-19 pandemic can be predicted by attachment style

Eder¹,

Nicholson²,

Stefańczyk³

et al. 2020

Preprint

Self Cite

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“…Details concerning procedure, analysis and variables have been described previously and are accessible online (see https://osf.io/db4px/; Eder et al, 2020, August 22;Eder et al, 2020, September 14).…”

Section: Methodsmentioning

confidence: 99%

“…Over the course of the seven weeks, the fear of existential economic losses remained fairly stable, whereas there was a slight decrease in mean fear of the virus and fear of shortages in supply (Figure 2), possibly indicating habituation. Actual environmental changes over the course of the study are depicted in Eder et al (2020, August 22).…”

Section: Slight Decrease In Fearmentioning

confidence: 99%

Food insecurity, hoarding behavior, and environmental harshness do not predict weight changes during the COVID-19 pandemic

Eder¹,

Stefańczyk²,

Pieniak³

et al. 2020

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The COVID-19 pandemic not only presents a medical and psychological stressor to many, but has also led to alterations that many perceived as threatening, such as reduced financial income and job insecurity. A symptom of collective worrying were so-called panic-or 'hamster' purchases, whereby people hoarded food following the introduction of governmental restrictions in response to the pandemic. This behavior indicated subjective 'food insecurity' in many individuals at a time when loss of income could potentially lead to real food insecurity for some. Since both perceived and actual food insecurity, as well as stress, are thought to facilitate weight gain as an adaptive response, this study aimed to investigate body weight during the pandemic. Specifically, we modeled the effects of both psychological and environmental factors such as food insecurity, hoarding behavior and loss of income, on body on weight as lockdown measures were introduced and loosened in four European countries. While we observe no systematic changes in body weight over the observed period, there was inter-individual variation in weight changes. However, contrary to our hypothesis, results show that neither food insecurity, hamster purchases, nor loss of income were predictors of weight change or weight/height ratio in our models, and none of our machine learning models outperformed a trivial predictor using the mean of all values for each prediction. In conclusion, this study does not add support for the notion that perceived food insecurity can predict weight gain or a higher weight/height ratio at a time of environmental uncertainties. This suggests that acute alterations in appetite reported upon triggering environmental harshness have no effect beyond that moment. Possibly life-history strategies acquired earlier in life remained stable during this crisis with respect to food consumption and weight.

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“…Identifying fear-provoking circumstances (stimulus) in work places in a pandemic will help tame anxieties and stress levels in healthy individuals and reduces the symptoms of those with pre-existing health disorders [9]. Similarly, it is opined that an important factor in understanding a population's response to any threat whatsoever is the fear it elicits [10] since fear is a significant predictor of behavioral changes and health-securing behaviours [11]. Studies on earlier pandemics show that anxiety, or the lack of it, is a significant impulse of behavior [12] as people with slight anxiety about a viral epidemic are less likely to be involved in precautionary hygienic behaviors like periodic hand-washing, seldom observe physical or social distancing stipulations, and are less likely to take vaccinations if available [12].…”

Section: IImentioning

confidence: 99%

“…by discriminating well between persons with and without dysfunctional anxiety using an optimized cut score of ≥ 9 (90% sensitivity and 85% specificity). The work of [10] seeks to discover the implications of fear emotion on students' and teachers' technology adoption during COVID-19 pandemic. The study adopts Google Meet as an educational social platform in private higher education institutes and data from the study were analyzed with partial least squares structural equation modeling (PLS-SEM) with machine learning supervised algorithm.…”

Section: IImentioning

confidence: 99%

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Olaleye¹

2022

IJRASET

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Data has indisputably proven overtime to have a better idea and with the surge of big data in the era of coronavirus, research initiatives in the field of data mining continues to leverage computational methodologies. Owing to the dreadful nature of the Omicron-variant, a fight or flight dilemma readily pervades college communities far reaching implications on work ethics of academic front-liners. This study therefore aim to gain insights from academia-sourced data to unravel fear-stimulus in college communities. The predictive analytics is carried out on college-based opinion poll. The Valence Aware Dictionary for Sentiment Reasoning algorithm is deployed for emotion analytics while the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are employed for topic modelling. The REPTree algorithm models the fear-spotting decision tree using 10-fold cross-validation. Experimental results shows a high performance metrics of 94.68% on Recall and Precision as the hand-washing attribute is returned as the most significant variable with highest information gain. Results of topic modelling likewise returns non-clinical precautionary measures as fear stimulus while the Vader sentiment analysis shows a 22.47%, 25.8%, and 51.73% positive, negative, and neutral polarity scores respectively, indicative of the academic front-liners’ pessimism towards effective safety measure compliance with non-clinical regulations. Keywords: COVID-19, Omicron, Sentiment Analysis, Topic Modelling.

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

Cited by 9 publications

References 31 publications

Securing your relationship: Quality of intimate relationships during the COVID-19 pandemic can be predicted by attachment style

Securing your relationship: Quality of intimate relationships during the COVID-19 pandemic can be predicted by attachment style

Food insecurity, hoarding behavior, and environmental harshness do not predict weight changes during the COVID-19 pandemic

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

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