Abstract
BACKGROUND: Extreme Risk Protection Orders (ERPOs) are civil court orders that prohibit firearm purchase and possession when someone is behaving dangerously and is at risk of harming themselves and/or others. As of June 2024, ERPOs are available in 21 states and the District of Columbia to prevent firearm violence. This paper describes the design and protocol of a six-state study of ERPO use. METHODS: The six states included are California, Colorado, Connecticut, Florida, Maryland, and Washington. During the 3-year project period (2020-2023), ERPO case files were obtained through public records requests or through agreements with agencies with access to these data in each state. A team of over four dozen research assistants from seven institutions coded 6628 ERPO cases, abstracting 80 variables per case under domains related to respondent characteristics, events and behaviors leading to ERPO petitions, petitioner types, and court outcomes. Research assistants received didactic training through an online learning management system that included virtual training modules, quizzes, practice coding exercises, and two virtual synchronous sessions. A protocol for gaining strong interrater reliability was used. Research assistants also learned strategies for reducing the risk of experiencing secondary trauma through the coding process, identifying its occurrence, and obtaining help. DISCUSSION: Addressing firearm violence in the U.S. is a priority. Understanding ERPO use in these six states can inform implementation planning and ERPO uptake, including promising opportunities to enhance safety and prevent firearm-related injuries and deaths. By publishing this protocol, we offer detailed insight into the methods underlying the papers published from these data, and the process of managing data abstraction from ERPO case files across the multi-state and multi-institution teams involved. Such information may also inform future analyses of this data, and future replication efforts. REGISTRATION: This protocol is registered on Open Science Framework ( https://osf.io/kv4fc/ ).