Family members and intimate partners [families] are uniquely positioned to reduce firearm access when their adult loved one is at risk of harming themselves or others, thereby preventing firearm injury. Yet we know little on how or when families are doing so, and if these efforts are safe and/or legal. To identify how and when families were reducing firearm access, a content analysis was conducted using police reports from Extreme Risk Protection Order [ERPO] petitions. These police reports were from the events that inspired police to file for the ERPO [precipitating event or PE] against the at-risk individual [respondent]. This content analysis identified and characterized the ways families tried to reduce the respondent’s firearm access at three time-points: before the PE, during the PE, and after police arrived at the PE. Next, supervised machine learning determined which respondent and PE characteristics were the most influential in predicting whether families reduced firearm access after police arrived at the PE. The content analysis identified that families reduced the respondent’s firearm access in numerous ways. Some families tried to prevent the respondent from accessing a firearm before the PE occurred, with a small number of police reports (2.54%) indicating such efforts. Fifteen percent of reports included information that families tried to keep firearms away from the respondent during the PE: Families hid firearms, sometimes in places where others could access them; asked the respondent for the firearms; and used physical force to try to take firearms. Just over a quarter (26.51%) of reports included information that families reduced firearm access after police officers arrived at the PE. Families gave officers consent and access to search for and remove firearms, asked police for help removing firearms, gave police firearms, and told officers they would continue to prevent firearm access after officers left the scene, including by temporarily storing firearms outside of the home. In the supervised machine learning model, two of the most influential variables predicting whether families reduced firearm access after police arrived at the PE were if the respondent was taken for an involuntary examination (which allows officers to remove firearms) and if officers told someone at the scene about ERPOs. The findings highlight that when families tried to prevent their loved ones from accessing firearms, they sometimes put themselves and others at risk of injury. Additionally, the temporary transfer of firearms to families outside the respondent’s home without a background check—while legal in Florida—may not be legal in other states with universal background check laws. The supervised machine learning models indicate which respondent and incident characteristics should be explored in more detail in future research. Finally, many of the supervised machine learning models had low sensitivity, indicating that indicating that there are likely other influential variables in predicting families reducing firearm access after police arrive that were not included in this study.
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