Domestic violence involving firearms increased during COVID-19 pandemic

To determine the trends in domestic violence and firearm domestic violence before and during the COVID-19 pandemic, the researchers used police-reported crime data from Jan. 1, 2018 through Dec. 31, 2020. The onset of the pandemic was considered to be Mar. 20, 2020. The cities examined were Chicago, IL; Cincinnati, OH; Kansas City, MO; Los Angeles, CA; and Nashville, TN. 

Estimated 6.5 million Californians know someone at risk of harming themselves, others

One in five adults in California, or an estimated 6.5 million people, are concerned that someone they know, usually a friend or family member, is at risk of harming themselves or others, according to new research published in Preventive Medicine.

Of the people perceived to be at risk for harming others, an estimated 19% were said to have access to firearms. For those perceived to be at risk of harming themselves, that number was approximately 11%.

Can records of firearm purchases help prevent mass shootings?

A first-of-its-kind study examining records of gun purchases in California found that mass and active shooters have distinct patterns of buying guns compared to other legal purchasers. The UC Davis Violence Prevention Research Program (VPRP) research was published in the Journal of Criminal Justice.

How you and everyone you know can help stop California mass shootings

“Four mass shootings in California in the past 10 days have left many wondering why this is still happening and what we can do to prevent it. In the Southern California city of Monterey Park, 11 people were killed in a ballroom by a man who was a frequent patron there. In Half Moon Bay, seven people were shot and killed at two agricultural sites by a co-worker.

Artificial intelligence could aid in evaluating parole decisions

To determine how effective the current system of risk-based parole is, researchers from the UC Davis Violence Prevention Research Program and the University of Missouri, Kansas City, used machine learning to analyze parole data from New York.

The machine learning algorithm found the predicted risks for those denied parole and those released are very similar. This suggests that low-risk individuals may have remained incarcerated, while high-risk individuals were released.

New study finds racial and ethnic differences in perception and use of ‘red flag’ laws

Research from the Violence Prevention Research Program finds racial and ethnic differences in how extreme risk protection orders (ERPOs) are perceived and used in California. The study, published in Preventive Medicine, found Black Californians perceived ERPOs as less appropriate and were less willing to ask a judge to approve one.

Free course to help health care providers reduce gun violence

The BulletPoints Project at UC Davis Health has launched a free online continuing education course to help physicians and other health care providers reduce gun violence. Although it is geared for clinicians, anyone can sign up.

Survey finds alarming trend toward political violence

The survey questions focused on three areas: beliefs regarding democracy and the potential for violence in the United States, beliefs regarding American society and institutions, and support for and willingness to engage in violence, including political violence.

Machine learning identifies gun buyers at risk of suicide

A study from the Violence Prevention Research Program (VPRP) at UC Davis suggests machine learning, a type of artificial intelligence, may help identify handgun purchasers who are at high risk of suicide. It also identified individual and community characteristics that are predictive of firearm suicide.

Extreme risk protection orders to prevent mass shootings: What does the research show?

Mass shootings are a source of shared anguish and outrage among Americans and are becoming more frequent. Extreme risk protection orders (ERPOs), also known as gun violence restraining orders or “red flag” laws, are designed to help prevent these shootings. Our experts note that: