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.

中文翻译：

---

复原力与灾难：面对不确定威胁的灵活适应


灾害会造成广泛的破坏、困难和人员伤亡。在本文中，我们首先考虑应对灾难的主流心理学方法及其对精神病理学（例如创伤后应激障碍）的狭隘关注。然后，我们回顾了更广泛方法的研究，该方法确定了异质性、高度可复制的结果轨迹，最常见的是稳定的心理健康或复原力。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，我们考虑弹性轨迹的相关性，并注意到它们预测未来弹性结果的能力极其有限。使用机器学习算法的研究改进了预测，但尚未阐明弹性适应背后的机制。为此，我们基于对监管灵活性的动机和机制组成部分的研究，提出了对弹性的更直接的心理学解释。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监管灵活性。