A collaborative approach to applying Natural Language Processing (NLP) to Domestic Homicide Reviews (DHRs): A study...

Area of Science:

• Public Health
• Computer Science
• Criminology

Background:

• Domestic Homicide Reviews (DHRs) are mandatory in England and Wales for domestic abuse-related deaths.
• Manual analysis of DHR narrative text is time-consuming and resource-intensive, limiting large-scale research.
• Natural Language Processing (NLP) offers a scalable computational approach to analyze large volumes of text data.

Purpose of the Study:

• To assess the feasibility of applying NLP techniques to analyze Domestic Homicide Reviews.
• To develop a collaborative approach combining NLP automation with practitioner expertise.
• To identify priority research questions for NLP analysis of DHRs.

Main Methods:

• Protocol outlines a study design for applying NLP to DHR data.
• Collaborative approach involving practitioners with contextual knowledge of domestic abuse.
• Details data access, retrieval, and analysis stages, including feasibility evaluation.
• Strategies for mitigating anticipated challenges in NLP application.

Main Results:

• The study protocol is established for assessing NLP feasibility in DHR analysis.
• A collaborative framework is proposed to ensure NLP outputs are sensitive and transparent.
• Priority research questions have been identified based on initial consultations.

Conclusions:

• NLP presents a viable and scalable alternative to manual analysis of DHRs.
• A collaborative approach is crucial for developing accountable and contextually relevant NLP tools.
• This research aims to enhance the understanding of domestic abuse deaths through advanced text analysis.