On April 20, 2010, the Deepwater Horizon oil rig exploded, and oil spilled from the breached well-head for months, leading to an unprecedented environmental disaster with implications for behavioral health. Disasters are thought to affect behavioral health, and social capital is thought to ameliorate behavioral health impacts after disasters, though empirical evidence is mixed. One possible explanation for the discrepancy in findings relates to the activation of social capital in different contexts. In a disaster context, certain types of social capital may be more beneficial than others, and these relationships could differ between those directly affected by the disaster and those who are unaffected. The goal of this study is to assess the relationships between different forms of social capital (community engagement, trust, and social support) on different behavioral health indicators (depression, anxiety, and alcohol misuse) using data from the first wave of the Survey of Trauma, Resilience, and Opportunity among Neighborhoods in the Gulf (STRONG), a probabilistic household telephone survey fielded 6 years after the onset of the Deepwater Horizon oil spill (DHOS). We employ a structural equation modeling approach where multiple social capital and behavioral health variables can be included and their pathways tested in the same model, comparing the results between those who reported experiencing disruptions related to the DHOS and those who did not. Among those who experienced the DHOS, social support was negatively associated with both depression (ß = –0.085; p = 0.011) and anxiety (ß = –0.097; p = 0.003), and among those who did not experience the DHOS, social support was positively associated with alcohol misuse (ß = 0.067; p = 0.035). When controlling for the other social capital variables, social support was the only form of social capital with a significant relationship to behavioral health, and these relationships differ based on whether or not a person experienced the disaster. This suggests that social capital does not have a uniformly ameliorative relationship with behavioral health in the aftermath of disasters.

近日，兰德公司（RAND Corporation）发布了一份名为《AI技术在K-12学校学生活动监测中应用于自杀风险考虑》的研究报告，深入探讨了AI技术在K-12学校学生活动监测中应用于自杀风险的问题。报告作者、兰德高级行为和社会科学家林赛·艾尔（Lynsay Ayer）等指出，为了应对普遍的青少年心理健康危机，一些K-12学校已经开始采用AI技术工具帮助学生识别自杀和自残风险。 该报告旨在为政策制定者、学校、技术开发商和政府提供关于AI技术在学校自杀风险监测中的使用情况、影响以及最佳实践的全面视角。报告发现，AI技术工具可以帮助K-12学校识别有自杀风险的学生，并为学校工作人员和家长提供一定程度的安慰。然而，这些工具也存在潜在的风险，可能会侵犯学生隐私，加剧现有不平等现象。 为此，报告作者提出了一些建议，包括：学校应与其社区进行反馈，明确通知家长和学生关于AI技术自杀风险监测的情况，并澄清退出程序；学校应建立有效的响应机制，跟踪学生警报后的结果；学校应与学生合作，帮助他们了解心理健康问题；政策制定者应资助学校和社区的心理健康支持，包括技术的使用；技术开发商应继续参与学校活动，将反馈融入其项目，并分享数据以评估AI监测软件对学生结果的影响。 该报告为K-12学校、政策制定者、技术开发商和政府在使用AI技术进行自杀风险监测时提供了重要的参考和指导。