Is It Possible for the Police to Predict and Prevent Domestic Murder?
Year: 2025
Type: article
Abstract: Domestic homicide, particularly intimate partner homicide (IPH), represents one of the most severe outcomes of domestic abuse, claiming hundreds of lives annually in the UK. According to the Office for National Statistics (ONS), in the year ending March 2022, 96 women and 21 men were killed by a partner or ex-partner, underscoring the urgent need for effective prevention strategies (ONS, 2022). Despite advances in policing, risk assessment tools, and public awareness campaigns, domestic homicide rates remain persistently high, reflecting systemic gaps in prediction and intervention mechanisms. This paper critically examines the predictive and preventive potential of existing tools, such as the DASH (Domestic Abuse, Stalking, and Honour-Based Violence) risk assessment form, alongside other policing strategies like Clare’s Law and pro-arrest policies. While these approaches aim to reduce domestic abuse and homicide, the study highlights their limited effectiveness and the disproportionate burden they place on victims to seek protection and engage with law enforcement. By integrating theoretical perspectives, including feminist and sociological frameworks, the study examines the structural inequalities and behavioural dynamics that underpin IPH. The paper focuses on three key objectives: identifying limitations in current predictive models, proposing systemic reforms to enhance prevention, and exploring the role of multi-agency collaboration in mitigating risk. Additionally, it emphasises the importance of addressing intersectional factors, such as ethnicity, class, and gender, in understanding and responding to domestic homicide. In doing so, this research seeks to contribute to ongoing efforts to reduce domestic homicide rates and promote safer communities.
Authors: Olivia Watkins, Tony Blockley
Institution: Leeds Trinity University
Language: English
Subfield: Health
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7219626414