| Title: |
The “Healthcare Workers’ Wellbeing [Benessere Operatori]” Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic |
| Authors: |
Perego, Gaia; Cugnata, Federica; Brombin, Chiara; Milano, Francesca; Preti, Emanuele; Di Pierro, Rossella; De Panfilis, Chiara; Madeddu, Fabio; Di Mattei, Valentina Elisabetta |
| Contributors: |
Perego, Gaia; Cugnata, Federica; Brombin, Chiara; Milano, Francesca; Preti, Emanuele; Di Pierro, Rossella; De Panfilis, Chiara; Madeddu, Fabio; Di Mattei, Valentina Elisabetta |
| Publisher Information: |
MDPI |
| Publication Year: |
2022 |
| Subject Terms: |
COVID-19; healthcare worker; mental health; mixed effects model; Random Effects/ Expectation Maximization (RE-EM) Tree |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:000794472400001; volume:11; issue:9; numberofpages:17; journal:JOURNAL OF CLINICAL MEDICINE; https://hdl.handle.net/20.500.11768/127918 |
| DOI: |
10.3390/jcm11092317 |
| Availability: |
https://hdl.handle.net/20.500.11768/127918; https://doi.org/10.3390/jcm11092317; https://www.mdpi.com/2077-0383/11/9/2317 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.C4ACF514 |
| Database: |
BASE |