Predicting Domestic Violence: Risk Factors and Clinical Assessments

Senior Capstone Experience by Brooke Brown ’21

Submitted to the Departments of Psychology and Biology

Advised by Dr. Lauren Littlefield and Dr. Robin Van Meter

Description: Due to the high prevalence of domestic violence (DV) in the United States and across the world, as well as the significant psychological and mental health issues that arise in both victims and perpetrators, it is essential for there to be both clinical and risk assessment for DV. The ability to identify individuals who are at greater risk for DV perpetration, in addition to the ability to accurately predict the risk of an offender to commit a violent act again, would serve numerous purposes in the criminal justice system, clinical settings, treatment options, and victim safety planning. Few comprehensive studies have approached the concept of DV prediction. This review identifies a particular set of characteristic factors and risk assessments that can be used for the prediction of DV perpetration and re-offense. This study finds that the presence of a history of violence within an individual, along with high scores on the Spousal Assault Risk Assessment, establishes the highest predictive value among DV recidivism. With the ability to accurately assess the risk of DV re-offense, these methods can be utilized to predict future violent acts. A careful assessment of other risk factors, including neurobiological and genetic, as well as other assessments are identified in this paper. In addition to an increasing quantity of assessments being developed, it is recommended for clinicians to assess both these factors and the assessments before decisions regarding prediction are made.

Read Brooke’s SCE below:

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s