Società Italiana di Anestesia, Analgesia, Rianimazione e Terapia Intensiva - SIAARTI, Rome, Italy.
Unit of Obstetric Anesthesia and Clinical Risk, Fatebenefratelli Hospital, Rome, Italy.
Minerva Anestesiol. 2023 Mar;89(3):188-196. doi: 10.23736/S0375-9393.22.16737-4. Epub 2022 Oct 25.
COVID-19 pandemic added additional burden upon healthcare systems and anesthesiology and intensive care physicians (AI) who possessed crucial expertise for dealing with the pandemic. Aim of the study was to uncover specific burnout patterns among Italian AI, exploring the hypothesis that burnout has a multicluster structure. Differences in social and professional characteristics between burnout patterns were explored.
One thousand and nine AI (658 women) members of the Società Italiana di Anestesia Analgesia Rianimazione e Terapia Intensiva (SIAARTI) working during COVID-19 pandemic participated. Sociodemographic, working information and burnout levels evaluated through Maslach Burnout Inventory (MBI) were collected.
According to the MBI cutoff, 39.7% and 25.8% of participants scored high in emotional exhaustion and depersonalization respectively, and 44.2% scored low in personal accomplishment. Cluster analysis highlighted four burnout profiles: resilience, detachment, burnout, and emotional reserve. The results showed that AI in the Resilience and Emotional Reserve groups were significantly older and more experienced than those in the Detachment and Burnout groups. Additionally, more of the individuals in the Resilience group were working in intensive care units and departments dedicated to COVID-19 patients. The Detachment group was comprised of more AI working in operating units, while the Burnout group contained a higher number of AI working in COVID-19 departments.
These findings highlight different burnout patterns in Italian AI: older age, more professional experience, and work in intensive care units and departments dedicated to COVID-19 seemed to be protective factors during the pandemic. This appears a first step to promote focused interventions.
COVID-19 大流行给医疗保健系统和麻醉学与重症监护医师（AI）带来了额外的负担，他们拥有应对大流行的关键专业知识。本研究旨在揭示意大利 AI 中特定的倦怠模式，探讨倦怠具有多聚类结构的假设。研究探索了倦怠模式之间在社会和职业特征方面的差异。
在 COVID-19 大流行期间，意大利麻醉学、镇痛、复苏和重症监护学会（SIAARTI）的 1009 名 AI（658 名女性）成员参与了这项研究。收集了人口统计学、工作信息和通过 Maslach 倦怠量表（MBI）评估的倦怠水平。
根据 MBI 截止值，39.7%和 25.8%的参与者在情绪耗竭和去人格化方面得分较高，44.2%在个人成就感方面得分较低。聚类分析突出了四种倦怠模式：韧性、超脱、倦怠和情绪储备。结果表明，在韧性和情绪储备组中的 AI 明显比在超脱和倦怠组中的 AI 年龄更大、经验更丰富。此外，更多处于韧性组的 AI 在重症监护病房和专门治疗 COVID-19 患者的部门工作。超脱组中的 AI 更多地在手术单元工作，而倦怠组中的 AI 更多地在 COVID-19 部门工作。
这些发现突出了意大利 AI 中不同的倦怠模式：年龄较大、专业经验较多、在重症监护病房和专门治疗 COVID-19 的部门工作，似乎是大流行期间的保护因素。这似乎是促进有针对性干预措施的第一步。
