COVID-19 pandemic had a negative impact on the mental health and well-being (WB) of citizens. This cross-sectional study included 4 waves of data collection aimed at identifying profiles of individuals with different levels of WB. The study included a representative stratified sample of 10,013 respondents in Italy. The WHO 5-item well-being scale (WHO-5) was used for the assessment of WB. Different supervised machine learning approaches (multinomial logistic regression, partial least-square discriminant analysis—PLS-DA—, classification tree—CT—) were applied to identify individual characteristics with different WB scores, first in waves 1–2 and, subsequently, in waves 3 and 4. Forty-one percent of participants reported “Good WB”, 30% “Poor WB”, and 28% “Depression”. Findings carried out using multinomial logistic regression show that Resilience was the most important variable able for discriminating the WB across all waves. Through the PLS-DA, Increased Unhealthy Behaviours proved to be the more important feature in the first two waves, while Financial Situation gained most relevance in the last two. COVID-19 Perceived Risk was relevant, but less than the other variables, across all waves. Interestingly, using the CT we were able to establish a cut-off for Resilience (equal to 4.5) that discriminated good WB with a probability of 65% in wave 4. Concluding, we found that COVID-19 had negative implications for WB. Governments should support evidence-based strategies considering factors that influence WB (i.e., Resilience, Perceived Risk, Healthy Behaviours, and Financial Situation).

Psychological well-being during the COVID-19 pandemic in Italy assessed in a four-waves survey

Gavaruzzi T.;
2022

Abstract

COVID-19 pandemic had a negative impact on the mental health and well-being (WB) of citizens. This cross-sectional study included 4 waves of data collection aimed at identifying profiles of individuals with different levels of WB. The study included a representative stratified sample of 10,013 respondents in Italy. The WHO 5-item well-being scale (WHO-5) was used for the assessment of WB. Different supervised machine learning approaches (multinomial logistic regression, partial least-square discriminant analysis—PLS-DA—, classification tree—CT—) were applied to identify individual characteristics with different WB scores, first in waves 1–2 and, subsequently, in waves 3 and 4. Forty-one percent of participants reported “Good WB”, 30% “Poor WB”, and 28% “Depression”. Findings carried out using multinomial logistic regression show that Resilience was the most important variable able for discriminating the WB across all waves. Through the PLS-DA, Increased Unhealthy Behaviours proved to be the more important feature in the first two waves, while Financial Situation gained most relevance in the last two. COVID-19 Perceived Risk was relevant, but less than the other variables, across all waves. Interestingly, using the CT we were able to establish a cut-off for Resilience (equal to 4.5) that discriminated good WB with a probability of 65% in wave 4. Concluding, we found that COVID-19 had negative implications for WB. Governments should support evidence-based strategies considering factors that influence WB (i.e., Resilience, Perceived Risk, Healthy Behaviours, and Financial Situation).
2022
de Girolamo G.; Ferrari C.; Candini V.; Buizza C.; Calamandrei G.; Caserotti M.; Gavaruzzi T.; Girardi P.; Habersaat K.B.; Lotto L.; Scherzer M.; Starace F.; Tasso A.; Zamparini M.; Zarbo C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/901014
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