Next, we tried to understand why and how these different beliefs, personality traits, and demographic factors affect a person`s likelihood of contracting COVID-19. In this study, predictors that take into account one`s own beliefs about context have developed during the pandemic (p.B perceived severity of the threat, knowledge of COVID-19). Other variables we use to predict disease are largely individual differences that our participants would likely have characterized before the pandemic (for example, . B political ideology, sensitivity to disgust, demographic factors). Consistent with the logic described above, we expected that many of these pre-existing individual differences would indirectly affect the contraction of the disease through intermediate perceptions and beliefs about the pandemic. Those most at risk of contracting COVID-19 are: All of our variables dealing with perceptions of the pandemic have been shown to be powerful predictors of testing positive for COVID-19 (see Table 1). This was knowledge about COVID-19 who believes that the threat of COVID-19 is (not) exaggerated, the extent to which there is concern about personally contracting the virus, and the perceived likelihood of contracting the virus. Since the last two were highly correlated (ra = 0.70), each was normalized and then averaged according to a composite measure that we call participants` perceived risk of contracting COVID-19. Overall, these three variables directly influence people`s perception of the pandemic and, as such, represent three possible ways in which some individual difference could be related to the risk of contracting COVID-19. Specifically, we hypothesize that our individual measures of difference indirectly predict disease by causing people to (a) develop less accurate (or inaccurate) knowledge about COVID-19, (b) minimize the overall risk of the pandemic, and/or (c) accurately perceive that they are at greater risk of contracting the virus. Jurkowitz, M., & Mitchell, A. Cable TV and COVID-19: The way Americans perceive the outbreak and view media coverage varies depending on the primary source of information. The Pew Research Center Journalism Project www.journalism.org/2020/04/01/cable-tv-and-covid-19-how-americans-perceive-the-outbreak-and-view-media-coverage-differ-by-main-news-source/ (2020).
Yes, it is possible. There are several reasons for “false negative” test results – meaning you really have COVID-19, even if the test result says you don`t. To determine which individual difference factors predicted COVID-19 infection, we performed a series of binary logistic regression analyses that examined the dichotomous COVID-19 status variable at follow-up (i.e., tested positive for COVID-19 or not tested at all) based on each of the predictive variables. This analysis allowed us to assess not only which variables predicted covid-19 infection, but also the magnitude of the effect for each prediction offered by the odds ratio – that is, how the probability of contracting COVID-19 changes with a change in unit in the predictive variable. The results are summarized in Table 1, which for each variable shows the number of participants who tested positive for the virus and regression statistics. (For ease of interpretation, all continuous predictive variables have been standardized).) Statistica 13.1. Dell Inc. (2016). Tulsa, OK 74104, USA and SPSS Amos Graphics 21. IBM Amos Development Corporation, Meadville, PA 16335, USA [40.41] was used for the analysis. First, descriptive statistics (means and standard deviations) were presented, and Pearson correlation coefficients were used to estimate the relationships between COVID-19 risk, self-efficacy, meaning of life, meaning, stress, and dimensions of subjective well-being. This allowed us to build a theoretical model that included the variables used in the present study.
Second, path analysis was used to test the theoretical model in which we examined the mediating role of meaning and stress in the relationships between COVID-19 risk, self-efficacy, meaning of life, and subjective well-being. The analysis was conducted for the cognitive (life satisfaction) and affective (positive and negative effects) dimensions of subjective well-being, distinguishing different facets of well-being. Direct and indirect effects were studied. Third, to examine the mediating effects of meaning-making and focus on the relationships between COVID-19 risk, self-efficacy, meaning of life, and dimensions of subjective well-being, the bootstrap procedure recommended by Preacher and Hayes  was used; 5000 samples started and 95% confidence intervals were included in the analysis. Standardized coefficients were used to compare the effects of independent variables and mediators on dependent variables. Research has shown that the meaning of life is positively related to the subjective well-being of American palliative care nurses  and Polish health workers , but negatively related to depression among Turkish health workers . The meaning of life had to be distinguished from other personal resources and was also related to well-being. By conveying important goals and values to individuals, reinterpreting their life experiences, and effectively directing their energies, the meaning of life can have a noticeable impact on how healthcare workers manage stress and maintain their job performance . Therefore, the meaning of life is very likely to influence how health care workers manage stress and maintain their well-being. Another important personal factor in health care is self-efficacy (a person`s belief in their ability to succeed in certain situations), which seems to significantly help health care workers effectively fulfill their obligations and perform their professional tasks. Self-efficacy has also been shown to be linked to the well-being of healthcare workers. .