This Mass Attacks Defense Toolkit can help reduce casualties from mass shootings and other violent attacks.
The fist phase of the Mass Attacks Defense Chain consists of deterring, detecting, and stopping plots to commit mass shootings and other mass attacks. A comprehensive community-based approach is proposed. This involves consultation, planning, and implementation of a plan that draws on the combined resources of the community in preparing for a mass attack. The Prevention team must partner with the public to build trust, encourage reporting, and provide support to turn those at risk away from violence. Law enforcement and other organizations, such as schools and businesses, benefit from assuming a community responsibility for reporting suspicious actions or statement that may lead to mass violence. The prevention team should follow all leads and provide help when needed to persons at risk of committing mass violence. Reassessments of responses to identified risks should be regularly conducted. The toolkit next provides an overview of resources for an Attack Prevention Framework that focuses on deterring attacks, initial detection of a warning sign, and follow-up. Special focus areas are schools and universities, mental health, conditions in the private sector, and the content of social media. Aspects addressed in deterring mass attacks include public awareness of warning signs, Initial detection, threat assessment, and follow-up actions. The confluence of leadership, accountability, and policies. Other toolkit components in the prevention phase address tools and resources to help build prevention teams, a case example for building a prevention team, deterring attacks, tools and resources for deterring attacks, initial detection, warning signs, findings on reporting initial detection, tools and resources on initial reporting, recommendations for initial detection, further reading on initial detection, and a case example of initial detection. This toolkit is followed by a Mass Attacks Defense Toolkit for mitigating an attack
- A Randomized Controlled Trial of the Impact of Body-Worn Cameras in the Loudoun County, VA, Adult Detention Center
- Research Rooted in Machine Learning Challenges Conventional Thinking About the Pathways to Violent Extremism
- Inferring Age at Death for Japanese and Thai Skeletal Samples under a Bayesian Framework of Analysis: A Test of Priors and Their Effects on Estimation