Getting through a COVID-19 winter: Physical coldness increases the perceived risk of coronavirus disease

Li, Heng

doi:10.1016/j.paid.2022.111799

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…After completing the questionnaires for power measurement, participants were asked to assess the relative likelihood of the risk of contracting six different types of diseases (i.e., coronavirus, high blood pressure, diabetes, gout, colorectal cancer, and hyperthyroidism), which were modeled after Li (2023). They indicated the percentile that corresponded to health risks, from 0 ( impossible ) to 100 ( certain to happen ).…”

Section: Methodsmentioning

confidence: 99%

“…More recently, an emerging stream of research has begun to investigate a number of seemingly unrelated factors that may influence the perceived risk of contagion, such as language and social perception (Lu & Schuldt, 2018; Schlager & Whillans, 2022). For example, Li (2023) investigated how conscious experiences of thermal transients could affect people's beliefs about the probability of contracting the new coronavirus. Specifically, they found that participants in the cold condition perceived their risk of getting the COVID‐19 disease to be greater than those in the control condition.…”

Section: Introductionmentioning

confidence: 99%

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Social power and perceived risk of contagious disease: High power leads to lower risk estimation of catching COVID‐19

2023

Risk Analysis

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An accurate estimation of COVID‐19 contagion risk is important in terms of understanding the dynamic of disease transmission and health behavior. Previous research has documented that many health‐related variables influence the risk estimation of communicable diseases. We expanded the current understanding by investigating whether health‐irrelevant factors—such as one's sense of power—can have a systematic and consequential impact on perceived risks of catching the coronavirus. Based on the social distance theory of power, we propose that people in a higher power position develop a greater sense of social distance than those in a lower power position, which may in turn predispose the former to think that they are less likely to catch contagious diseases from other people. In Study 1, we provided correlational evidence that the personal sense of power was associated with the underestimation of contagion probability in Chinese university students. In Study 2, we established the causal relationship between power and concerns for contagious diseases in nonstudent adults and revealed the mediating role of social distance in the observed effect. Overall, these results, for the first time, indicate that power can elevate perceived social distance, exerting downstreaming effects on health cognition during the COVID‐19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social power and perceived risk of contagious disease: High power leads to lower risk estimation of catching COVID‐19

2023

Risk Analysis

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“…Thus, the main objective of this study is to propose and empirically test the comprehensive model of individual differences in demographic characteristics, health-related issues, and beliefs that influence the intentions to vaccinate for COVID-19 in the United States. These individual differences and perceived risks have been regarded to play important roles specific to infectious diseases such as COVID-19 (Edwards et al, 2022;Li, 2022). Further, we use the findings in this study to develop a multiple regression to mathematically model to generalize and predict intention to vaccinate for COVID-19 based on the demographics and characteristics the individual possesses.…”

Section: Introductionmentioning

confidence: 93%

Utilizing Multiple Linear Regression Models to Predict Intentions to Vaccinate for COVID-19

Kulviwat¹

2023

JHSSR

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The purpose of this research is to develop a multiple linear regression model to predict the intention to vaccinate for COVID-19. An anonymous cross-sectional online survey utilizing Qualtrics software was conducted. Descriptive statistics and inter-correlations between predictors and criterion variables were presented. Multiple linear regression was used to analyze associations among predictors and criterion variables. Overall, results indicated that while 79% of participants reported their intentions to get vaccinated against COVID-19 when a vaccine becomes available, 21% reported not being likely to. Further, results showed that six socio-demographic, health-related, and belief factors had positive effects on intentions to vaccinate for COVID-19. Specifically, the predictive variables of the belief in the effectiveness of the COVID-19 vaccine, political party affiliation, previous influenza vaccinations, employment status, perceived knowledge of COVID-19 and the COVID-19 vaccine, as well as education level have crucial roles in predicting the dependent variable of the intention to vaccinate for COVID-19. This research contributes to our understanding of the various factors that influence the decision to vaccinate for COVID-19. Overall, the proposed regression model with the variables present in this study represents a strong effect and explains the proportion of the variability in the intention to vaccinate with over 70% accuracy. These results have important practical as well as theoretical implications for public health policymakers. With significant percentages of the population that are still hesitant to vaccinate, future studies should focus on finding this missing link and implementing any social/public health policies to level up individual intentions.

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How Wildfire Experience Influences Intentions to Adopt Protective Measures over Time

2023

Journal of Loss and Trauma

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Getting through a COVID-19 winter: Physical coldness increases the perceived risk of coronavirus disease

Cited by 4 publications

References 43 publications

Social power and perceived risk of contagious disease: High power leads to lower risk estimation of catching COVID‐19

Social power and perceived risk of contagious disease: High power leads to lower risk estimation of catching COVID‐19

Utilizing Multiple Linear Regression Models to Predict Intentions to Vaccinate for COVID-19

How Wildfire Experience Influences Intentions to Adopt Protective Measures over Time

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