RAPID: Coping in Families Affected by California Wildfires and Flooding

RAPID：应对受加州野火和洪水影响的家庭

• 批准号: 1827230
• 负责人: Charles Benight
• 金额: $ 15.86万
• 依托单位: University of Colorado at Colorado Springs
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827230&HistoricalAwards=false
• 关键词: RAPID; Coping; Families; Affected; California

Severe natural disasters carry great economic costs and threaten the health and well-being of many people. Severe natural disasters during the past decade include unprecedented flooding, catastrophic hurricanes, and devastating wildfires. The worst wildfire in California history struck Ventura and Santa Barbara counties in December 2017. The Thomas Fire in those counties burned 282,000 acres, took two lives, and incinerated or damaged over one thousand structures. Because of its location to major population centers, over 100,000 residents were evacuated, many more than once. In the aftermath of the fire, rain produced a massive mudslide cascading through the city of Montecito killing 23 people and destroying 115 homes. These catastrophic events within a geographically concentrated region offer a unique opportunity to better understand how individuals and families cope with and recover from profound stress. The focus of this project is to gain a richer understanding of how individuals and families manage post-disaster recovery and what predicts both positive and negative outcomes. By bringing together a multi-disciplinary team of computer scientists and disaster psychologists, new theories and computational methods will be evaluated to better understand human response to extreme events and to inform the development of future interventions.Using Smart Phone technology and online surveys over a six-month period of time, 100 parent/child pairs and 100 non-parent survivors in Ventura and Santa Barbara counties will be followed. The surveys and the daily "check-ins" will provide critical data on how people cope with stress across time. By cultivating a very rich set of data on the same individuals over time, it will be possible to discern patterns of recovery in ways that past research has not done. Measuring outcomes on a daily basis will generate significant data and support the use of "big data" analysis methods (e.g., machine learning techniques) that can identify unique changes or shifts in functioning predicted by the guiding theoretical framework (self-regulation shift theory). This project extends what is currently understood related to post-disaster recovery by targeting key coping mechanisms of change and highlighting possible critical targets for supportive interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

严重的自然灾害具有巨大的经济成本，并威胁着许多人的健康和福祉。 在过去的十年中，严重的自然灾害包括前所未有的洪水，灾难性的飓风和毁灭性的野火。 加利福尼亚历史上最糟糕的野火在2017年12月袭击了文图拉和圣塔芭芭拉县。这些县的托马斯大火烧毁了282,000英亩的土地，夺走了两次生命，并焚化或损坏了一千个建筑物。由于其位置到主要人口中心，因此撤离了100,000多名居民，多次撤离。 大火之后，雨水通过蒙特西托市造成了巨大的泥石流，杀死了23人并摧毁了115栋房屋。 这些灾难性的事件在地理位置集中的地区提供了一个独特的机会，可以更好地了解个人和家庭如何应对并从深刻的压力中恢复。 该项目的重点是对个人和家庭如何管理后策划后的恢复以及预测积极和负面结果的原因有更丰富的了解。通过汇总一个计算机科学家和灾难心理学家的跨学科团队，将评估新的理论和计算方法，以更好地了解人类对极端事件的反应，并为未来的干预措施提供信息。在六个月的时间内，智能电话技术和在线调查，在100个父母/子女成绩中，100个父母和儿童和100个非父母的幸存者在Ventura和Santa Barbara Courties中都将遵循。 调查和每日“签到”将提供有关人们如何应对压力的关键数据。 随着时间的流逝，通过在同一个人上培养一组非常丰富的数据，可以以过去的研究没有做的方式来辨别恢复模式。 每天测量结果将产生重要的数据，并支持使用“大数据”分析方法（例如机器学习技术），该方法可以识别通过指导理论框架（自我调节偏移理论）所预测的唯一变化或功能的变化。 该项目通过针对变革的关键应对机制并突出了支持性干预措施的关键目标，从而扩展了与垃圾后恢复相关的理解。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来进行评估的。