1. Risk and protective factors associated with mental health status in an Italian sample of students during the fourth wave of COVID-19 pandemic.
- Author
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Lanfredi, Mariangela, Dagani, Jessica, Geviti, Andrea, Di Cosimo, Federica, Bussolati, Maria, Rillosi, Luciana, Albini, Donatella, Pizzi, Marina, Ghidoni, Roberta, Fazzi, Elisa, Vita, Antonio, and Rossi, Roberta
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FOOD habits , *PSYCHOLOGY of college students , *SCIENTIFIC observation , *CROSS-sectional method , *MENTAL health , *HEALTH status indicators , *MANN Whitney U Test , *QUANTITATIVE research , *RISK assessment , *SURVEYS , *SEX distribution , *PSYCHOLOGY of high school students , *MENTAL depression , *LONELINESS , *PATHOLOGICAL psychology , *QUESTIONNAIRES , *DESCRIPTIVE statistics , *RESEARCH funding , *STUDENT attitudes , *ANXIETY , *ANGER , *CLUSTER analysis (Statistics) , *LOGISTIC regression analysis , *FAMILY relations , *COVID-19 pandemic , *PSYCHOLOGICAL resilience , *PSYCHOLOGICAL distress - Abstract
Background: It is well known that the COVID-19 pandemic has caused a global health crisis, especially for young people. However, most studies were conducted during the first waves of the pandemic. Few Italian studies specifically attempted to broadly assess young people's mental health status during the fourth wave of the pandemic. Methods: This study aimed at evaluating the mental health status among a group of Italian adolescents and young adults during the fourth wave of the COVID-19 pandemic. 11,839 high school students and 15,000 university students (age range 14–25) were asked to complete a multidimensional online survey, of which 7,146 (26,6%) agreed to participate. The survey also included standardized measures for depression, anxiety, anger, somatic symptoms, resilience, loneliness and post-traumatic growth. Two separate clusters were identified through cluster analysis. Random forest, classification tree and logistic regressions analyses were applied to identify factors associated to a good or a poor level of mental health and, thus, to define students' mental health profiles. Results: Overall, the students in our sample showed high levels of psychopathology. The clustering methods performed identified two separate clusters reflecting groups of students with different psychological features, that we further defined as "poor mental health" and "good mental health". The random forest and the logistic regressions found that the most discriminating variables among those two groups were: UCLA Loneliness Scale score, self-harm behaviors, Connor-Davidson Resilience Scale-10 score, satisfaction with family relationships, Fear of COVID-19 Scale score, gender and binge eating behaviors. The classification tree analysis identified students' profiles, showing that, globally, poor mental health was defined by higher scores of loneliness and self-harm, followed by being of female gender, presenting binge eating behaviors and, finally, having unsatisfying family relationships. Conclusions: The results of this study confirmed the major psychological distress caused by the COVID-19 pandemic in a large sample of Italian students, and provided further insights regarding those factors associated with a good or poor mental health status. Our findings suggest the importance of implementing programs targeting aspects that have been found to be associated to a good mental health. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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