恢复力和灾害:在面对不确定的威胁时灵活适应
George A Bonanno1, Shuquan Chen1, Rohini Bagrodia1
1Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, NY, USA; email: gab38@tc.columbia.edu, sc4173@tc.columbia.edu, rb3017@tc.columbia.edu.
Annual review of psychology
|August 11, 2023
概括
大多数人在灾难后表现出弹性,而不是精神病理. 了解监管灵活性是解释和预测创伤事件后心理健康结果的关键.
科学领域:
- 心理学 心理学 心理学
- 灾难心理健康 灾难心理健康
- 计算精神病学是一种计算精神病学.
背景情况:
- 传统的灾难心理学往往仅限于精神病理学,例如创伤后应激障碍.
- 研究揭示了灾难后多样化,一致的结果轨迹,弹性是最常见的.
- 以前的灾害类型,包括COVID-19大流行,表现出类似的弹性模式.
研究的目的:
- 审查对灾难结果的心理方法,将精神病理与弹性轨迹对比起来.
- 检查弹性相关的预测局限性,并探索机器学习的作用.
- 提出监管灵活性作为理解和预测性的核心心理机制.
主要方法:
- 对灾难结果的心理方法的文献综述.
- 分析各种灾害类型的轨迹研究.
- 检查机器学习应用在预测灾害适应方面的应用.
- 监管灵活性作为一个关键的解释结构的理论建议.
主要成果:
- 恢复力,定义为稳定的心理健康,是灾难后最常见的结果.
- 弹性相关性对未来结果的预测能力有限.
- 机器学习可以提高预测,但不能完全解释适应机制.
- 监管灵活性为弹性提供了一个有希望的心理解释.
结论:
- 抗灾能力是灾难后的常见和可复制的结果.
- 了解适应机制,特别是监管灵活性,至关重要.
- 未来的研究应该利用计算方法实时研究监管灵活性.
关键词:
在 COVID-19 疫情中,创伤后应激障碍 (PTSD) 是一种成本-收益的权衡权衡.这是一场灾难性的灾难.灵活性 灵活性 灵活性机器学习是机器学习.创伤后应激障碍 创伤后应激障碍恢复力 恢复力它们的轨迹和轨迹.一个创伤的创伤创伤.更多相关视频
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