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The “Healthcare Workers’ Wellbeing [Benessere Operatori]” Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic

Academic Article
Publication Date:
2022
Short description:
The “Healthcare Workers’ Wellbeing [Benessere Operatori]” Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic / Perego, G., Cugnata, F., Brombin, C., Milano, F., Preti, E., Di Pierro, R., De Panfilis, C., Madeddu, F., Di Mattei, V.E.. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 11:9(2022). [10.3390/jcm11092317]
abstract:
Background: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The “Healthcare workers’ wellbeing (Benessere Operatori)” project aimed at identifying psychological and personal factors, influencing individuals’ responses to the COVID-19 pandemic. Methods: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. Results: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers’ mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. Conclusions: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.
Iris type:
1.1 Articolo in rivista
List of contributors:
Perego, Gaia; Cugnata, Federica; Brombin, Chiara; Milano, Francesca; Preti, Emanuele; Di Pierro, Rossella; De Panfilis, Chiara; Madeddu, Fabio; Di Mattei, Valentina Elisabetta
Authors of the University:
BROMBIN CHIARA
CUGNATA FEDERICA
DI MATTEI VALENTINA ELISABETTA
PEREGO GAIA
Handle:
https://iris.unisr.it/handle/20.500.11768/127918
Full Text:
https://iris.unisr.it//retrieve/handle/20.500.11768/127918/96128/jcm-11-02317-v2.pdf
Published in:
JOURNAL OF CLINICAL MEDICINE
Journal
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URL

https://www.mdpi.com/2077-0383/11/9/2317
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