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

Journal of Clinical Medicine

Francesca Milano¹  Chiara De Panfilis²  Rossella Di Pierro³  Emanuele Preti³  Gaia Perego³  Fabio Madeddu³  Chiara Brombin⁴  Federica Cugnata⁴  Valentina Elisabetta Di Mattei⁴ 

[1] Clinical and Health Psychology Unit, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy;Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy;Department of Psychology, University of Milano-Bicocca, 20126 Milan, Italy;School of Psychology, Vita-Salute San Raffaele University, 20132 Milan, Italy;

关键词: COVID-19;    healthcare workers;    mental health;    mixed effects model;    Random Effects/Expectation Maximization (RE-EM) Tree;   

DOI  :  10.3390/jcm11092317

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown