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      Perceived risk, political polarization, and the willingness to follow COVID-19 mitigation guidelines

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          Abstract

          Objective

          Risk assessment and response is important for understanding human behavior. The divisive context surrounding the coronavirus pandemic inspires our exploration of risk perceptions and the polarization of mitigation practices (i.e., the degree to which the behaviors of people on the political “Left” diverge from those on the “Right”). Specifically, we investigate the extent to which the political polarization of willingness to comply with mitigation behaviors changes with risk perceptions.

          Method

          Analyses use data from two sources: an original dataset of Twitter posts and a nationally-representative survey. In the Twitter data, negative binomial regression models are used to predict mitigation intent measured using tweet counts. In the survey data, logit models predict self-reported mitigation behavior (vaccination, masking, and social distancing).

          Results

          Findings converged across both datasets, supporting the idea that the links between political orientation and willingness to follow mitigation guidelines depend on perceived risk. People on the Left are more inclined than their Right-oriented colleagues to follow guidelines, but this polarization tends to decrease as the perceived risk of COVID-19 intensifies. Additionally, we find evidence that exposure to COVID-19 infections sends ambiguous signals about the risk of the virus while COVID-19 related deaths have a more consistent impact on mitigation behaviors.

          Conclusions

          Pandemic-related risks can create opportunities for perceived “common ground,” between the political “Right” and “Left.” Risk perceptions and politics interact in their links to intended COVID-19 mitigation behavior (as measured both on Twitter and in a national survey). Our results invite a more complex interpretation of political polarization than those stemming from simplistic analyses of partisanship and ideology.

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          Most cited references80

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          The Fear of COVID-19 Scale: Development and Initial Validation

          Background The emergence of the COVID-19 and its consequences has led to fears, worries, and anxiety among individuals worldwide. The present study developed the Fear of COVID-19 Scale (FCV-19S) to complement the clinical efforts in preventing the spread and treating of COVID-19 cases. Methods The sample comprised 717 Iranian participants. The items of the FCV-19S were constructed based on extensive review of existing scales on fears, expert evaluations, and participant interviews. Several psychometric tests were conducted to ascertain its reliability and validity properties. Results After panel review and corrected item-total correlation testing, seven items with acceptable corrected item-total correlation (0.47 to 0.56) were retained and further confirmed by significant and strong factor loadings (0.66 to 0.74). Also, other properties evaluated using both classical test theory and Rasch model were satisfactory on the seven-item scale. More specifically, reliability values such as internal consistency (α = .82) and test–retest reliability (ICC = .72) were acceptable. Concurrent validity was supported by the Hospital Anxiety and Depression Scale (with depression, r = 0.425 and anxiety, r = 0.511) and the Perceived Vulnerability to Disease Scale (with perceived infectability, r = 0.483 and germ aversion, r = 0.459). Conclusion The Fear of COVID-19 Scale, a seven-item scale, has robust psychometric properties. It is reliable and valid in assessing fear of COVID-19 among the general population and will also be useful in allaying COVID-19 fears among individuals.
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            Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

            Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
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              The Health Belief Model: a decade later.

              Since the last comprehensive review in 1974, the Health Belief Model (HBM) has continued to be the focus of considerable theoretical and research attention. This article presents a critical review of 29 HBM-related investigations published during the period of 1974-1984, tabulates the findings from 17 studies conducted prior to 1974, and provides a summary of the total 46 HBM studies (18 prospective, 28 retrospective). Twenty-four studies examined preventive-health behaviors (PHB), 19 explored sick-role behaviors (SRB), and three addressed clinic utilization. A "significance ratio" was constructed which divides the number of positive, statistically-significant findings for an HBM dimension by the total number of studies reporting significance levels for that dimension. Summary results provide substantial empirical support for the HBM, with findings from prospective studies at least as favorable as those obtained from retrospective research. "Perceived barriers" proved to be the most powerful of the HBM dimensions across the various study designs and behaviors. While both were important overall, "perceived susceptibility" was a stronger contributor to understanding PHB than SRB, while the reverse was true for "perceived benefits." "Perceived severity" produced the lowest overall significance ratios; however, while only weakly associated with PHB, this dimension was strongly related to SRB. On the basis of the evidence compiled, it is recommended that consideration of HBM dimensions be a part of health education programming. Suggestions are offered for further research.
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                Author and article information

                Journal
                Soc Sci Med
                Soc Sci Med
                Social Science & Medicine (1982)
                Published by Elsevier Ltd.
                0277-9536
                1873-5347
                2 June 2022
                2 June 2022
                : 115091
                Affiliations
                [a ]Penn State Departments of Political Science and African American Studies, 308 Pond Laboratory, University Park, PA, 16802, USA
                [b ]Penn State Department of Political Science and the Center for Social Data Analytics, Pond Laboratory, University Park, PA, 16802, USA
                [c ]Penn State Donald P. Bellisario College of Communications and the Center for Social Data Analytics, 8 Carnegie Building University Park, PA, 16802, USA
                [d ]Penn State Department of Statistics and the Center for Social Data Analytics, 122 Chemistry Building University Park, PA, 16802, USA
                [e ]Penn State Department of Sociology and Criminology and the Center for Social Data Analytics, 1001 Oswald Tower University Park, PA, 16802, USA
                Author notes
                []Corresponding author. Penn State Department of Political Science, Pond Laboratory, University Park, PA, 16802, USA.
                Article
                S0277-9536(22)00397-5 115091
                10.1016/j.socscimed.2022.115091
                9161674
                35690035
                17d588cf-4453-4911-92fd-c080d0611f4c
                © 2022 Published by Elsevier Ltd.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 4 January 2022
                : 24 May 2022
                : 27 May 2022
                Categories
                Article

                Health & Social care
                covid-19,ideology,political polarization,adherence,disease mitigation behavior,risk perception,social media,public opinion

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