Introduction: The present study investigates the lockdown experience in Italy during the COVID-19 pandemic within a positive psychology framework, focusing on the protective role of the positive ...anticipatory states: optimism and hope. Aims: The aims were to verify if and how optimism and hope influenced people's psychological wellbeing and their risk perception of the situation, addressing how individuals portrayed the present and how they imagined the future after the lockdown. Method: Based on the differences between the two constructs, as from the literature, the hypothesis is that individuals with higher levels of optimism would report positive but hazy future scenarios and lower levels of risk perception about the future. Therefore 1,471 participants received an online survey, which was administered as a set of questionnaires investigating three areas: demographic information, psychological wellbeing, and risk of contagion perception. Results: The results showed that positive anticipatory states are positively associated with psychological wellbeing. Moreover, the results highlighted the relationship between optimism and risk perception regarding future scenarios. Conclusions: The presented predictive model demonstrated that positive anticipatory states, sex, and age had a central role in determining the psychological wellbeing during the first wave of the pandemic events in Italy. Practical implications are discussed. Keywords: hope, optimism, wellbeing, risk perception, COVID-19
Purpose: To statistically validate the PREM (Pandemic Risk Exposure Measurement) model devised in a previous paper by the authors and determine the model's relationship with the level of current ...COVID-19 cases (NLCC) and the level of current deaths related to COVID-19 (NLCD) based on the real country data. Methods: We used perceived variables proposed in a previous study by the same lead authors and applied the latest available real data values for 154 countries. Two endogenous real data variables (NLCC) and (NLCD) were added. Data were transformed to measurable values using a Likert scale of 1 to 5. The resulting data for each variable were entered into SPSS (Statistical Package for the Social Sciences) version 26 and Amos (Analysis of a Moment Structures) version 21 and subjected to statistical analysis, specifically exploratory factor analysis, Cronbach's alpha and confirmatory factor analysis. Results: The results obtained confirmed a 4-factor structure and that the PREM model using real data is statistically reliable and valid. However, the variable Q14--hospital beds available per capita (1000 inhabitants) had to be excluded from the analysis because it loaded under more than one factor and the difference between the factor common variance was less than 0.10. Moreover, its Factor 1 and Factor 3 with NLCC and Factor 1 with NLCD showed a statistically significant relationship. Conclusion: Therefore, the developed PREM model moves from a perception-based model to reality. By proposing a model that allows governments and policymakers to take a proactive approach, the negative impact of a pandemic on the functioning of a country can be reduced. The PREM model is useful for decision-makers to know what factors make the country more vulnerable to a pandemic and, if possible, to manage or set tolerances as part of a preventive measure. Keywords: COVID-19, pandemic risk exposure, PREM, proactive, vulnerability
Purpose: To statistically validate the PREM (Pandemic Risk Exposure Measurement) model devised in a previous paper by the authors and determine the model's relationship with the level of current ...COVID-19 cases (NLCC) and the level of current deaths related to COVID-19 (NLCD) based on the real country data. Methods: We used perceived variables proposed in a previous study by the same lead authors and applied the latest available real data values for 154 countries. Two endogenous real data variables (NLCC) and (NLCD) were added. Data were transformed to measurable values using a Likert scale of 1 to 5. The resulting data for each variable were entered into SPSS (Statistical Package for the Social Sciences) version 26 and Amos (Analysis of a Moment Structures) version 21 and subjected to statistical analysis, specifically exploratory factor analysis, Cronbach's alpha and confirmatory factor analysis. Results: The results obtained confirmed a 4-factor structure and that the PREM model using real data is statistically reliable and valid. However, the variable Q14--hospital beds available per capita (1000 inhabitants) had to be excluded from the analysis because it loaded under more than one factor and the difference between the factor common variance was less than 0.10. Moreover, its Factor 1 and Factor 3 with NLCC and Factor 1 with NLCD showed a statistically significant relationship. Conclusion: Therefore, the developed PREM model moves from a perception-based model to reality. By proposing a model that allows governments and policymakers to take a proactive approach, the negative impact of a pandemic on the functioning of a country can be reduced. The PREM model is useful for decision-makers to know what factors make the country more vulnerable to a pandemic and, if possible, to manage or set tolerances as part of a preventive measure. Keywords: COVID-19, pandemic risk exposure, PREM, proactive, vulnerability
PDF
