Department of Human Sciences "R. Massa", University of Milano-Bicocca, 20126 Milan, Italy.
Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy.
Int J Environ Res Public Health. 2023 Mar 2;20(5):4482. doi: 10.3390/ijerph20054482.
COVID-19 broke out in China in December 2019 and rapidly became a worldwide pandemic that demanded an extraordinary response from healthcare workers (HCWs). Studies conducted during the pandemic observed severe depression and PTSD in HCWs. Identifying early predictors of mental health disorders in this population is key to informing effective treatment and prevention. The aim of this study was to investigate the power of language-based variables to predict PTSD and depression symptoms in HCWs. One hundred thirty-five HCWs (mean age = 46.34; SD = 10.96) were randomly assigned to one of two writing conditions: expressive writing (EW = 73) or neutral writing (NW = 62) and completed three writing sessions. PTSD and depression symptoms were assessed both pre- and post-writing. LIWC was used to analyze linguistic markers of four trauma-related variables (cognitive elaboration, emotional elaboration, perceived threat to life, and self-immersed processing). Changes in PTSD and depression were regressed onto the linguistic markers in hierarchical multiple regression models. The EW group displayed greater changes on the psychological measures and in terms of narrative categories deployed than the NW group. Changes in PTSD symptoms were predicted by cognitive elaboration, emotional elaboration, and perceived threat to life; changes in depression symptoms were predicted by self-immersed processing and cognitive elaboration. Linguistic markers can facilitate the early identification of vulnerability to mental disorders in HCWs involved in public health emergencies. We discuss the clinical implications of these findings.
2019 年 12 月，COVID-19 在中国爆发，并迅速成为全球大流行疾病，这要求医护人员（HCWs）做出非凡的反应。大流行期间的研究观察到 HCWs 中存在严重的抑郁和创伤后应激障碍（PTSD）。确定该人群心理健康障碍的早期预测因素对于提供有效的治疗和预防至关重要。本研究旨在探讨基于语言的变量对预测 HCWs 中 PTSD 和抑郁症状的能力。135 名 HCWs（平均年龄=46.34；SD=10.96）被随机分配到两种写作条件之一：表达性写作（EW=73）或中性写作（NW=62），并完成了三次写作。在写作前后都评估了 PTSD 和抑郁症状。LIWC 用于分析与四个与创伤相关的变量（认知详细描述、情绪详细描述、感知对生命的威胁和自我沉浸处理）相关的语言标记。在分层多元回归模型中，将 PTSD 和抑郁的变化回归到语言标记上。EW 组在心理测量和叙事类别上的变化大于 NW 组。PTSD 症状的变化由认知详细描述、情绪详细描述和感知对生命的威胁预测；抑郁症状的变化由自我沉浸处理和认知详细描述预测。语言标记可以促进对参与公共卫生突发事件的 HCWs 中精神障碍易感性的早期识别。我们讨论了这些发现的临床意义。
