Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA; email:
Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, USA; email:
Annu Rev Psychol. 2024 Jan 18;75:573-599. doi: 10.1146/annurev-psych-011123-024224. Epub 2023 Aug 11.
Disasters cause sweeping damage, hardship, and loss of life. In this article, we first consider the dominant psychological approach to disasters and its narrow focus on psychopathology (e.g., posttraumatic stress disorder). We then review research on a broader approach that has identified heterogeneous, highly replicable trajectories of outcome, the most common being stable mental health or resilience. We review trajectory research for different types of disasters, including the COVID-19 pandemic. Next, we consider correlates of the resilience trajectory and note their paradoxically limited ability to predict future resilient outcomes. Research using machine learning algorithms improved prediction but has not yet illuminated the mechanism behind resilient adaptation. To that end, we propose a more direct psychological explanation for resilience based on research on the motivational and mechanistic components of regulatory flexibility. Finally, we consider how future research might leverage new computational approaches to better capture regulatory flexibility in real time.
灾难会造成大规模的破坏、困难和生命损失。在本文中,我们首先考虑灾难的主流心理方法及其对精神病理学的狭隘关注(例如创伤后应激障碍)。然后,我们回顾了更广泛方法的研究,该方法确定了结果的异质且高度可复制的轨迹,最常见的是稳定的心理健康或韧性。我们回顾了不同类型灾难的轨迹研究,包括 COVID-19 大流行。接下来,我们考虑了韧性轨迹的相关性,并注意到它们预测未来有韧性的结果的能力非常有限。使用机器学习算法的研究提高了预测能力,但尚未阐明韧性适应的机制。为此,我们基于对调节灵活性的动机和机械成分的研究,提出了一个更直接的关于韧性的心理解释。最后,我们考虑未来的研究如何利用新的计算方法来更好地实时捕捉调节灵活性。