Perego Gaia, Cugnata Federica, Brombin Chiara, Milano Francesca, Preti Emanuele, Di Pierro Rossella, De Panfilis Chiara, Madeddu Fabio, Di Mattei Valentina Elisabetta
Department of Psychology, University of Milano-Bicocca, 20126 Milan, Italy.
School of Psychology, Vita-Salute San Raffaele University, 20132 Milan, Italy.
J Clin Med. 2022 Apr 21;11(9):2317. doi: 10.3390/jcm11092317.
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.
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.
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.
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.
新冠疫情迫使医护人员在前所未有的危急情况下工作,使本就棘手且充满压力的工作环境更加恶化。“医护人员福祉(Benessere Operatori)”项目旨在确定影响个人应对新冠疫情反应的心理和个人因素。
291名医护人员参与了该项目,他们两次在线回答问卷(在新冠疫情第一波之后和第二波期间),并完成了关于社会人口统计学和工作相关信息、抑郁焦虑压力量表-21、失眠严重程度指数、事件影响量表修订版、状态-特质愤怒表达量表-2、马氏职业倦怠量表、感知社会支持多维量表和简易应对方式问卷的问题。
较高的担忧水平、较差的工作条件、既往精神疾病史、身为护士、年龄较大以及回避和情绪聚焦的应对策略似乎是医护人员心理健康的风险因素。高水平的感知社会支持、参加应急培训以及问题聚焦的应对策略起到保护作用。
一种创新且更灵活的数据挖掘统计方法(即针对重复测量数据的回归树方法)使我们能够识别风险因素并得出分类规则,这可能有助于为医护人员实施有针对性的干预措施。