Disclosure of violence in remote and face-to-face consultations – a data science investigation

Violence, whether experienced within intimate relationships, families, or communities, has profound consequences for physical and mental health. Disclosure of violence in healthcare settings is a critical step toward intervention, support, and recovery. However, factors such as stigma, fear, and healthcare access can significantly influence whether and how individuals disclose experiences of violence. With the increasing adoption of remote healthcare consultations, there is a need to understand how disclosure patterns may differ between remote and face-to-face interactions.

Mental health research is being transformed by the availability of large-scale data resources, particularly through electronic health records (EHRs). At the NIHR Maudsley Biomedical Research Centre, the Clinical Record Interactive Search (CRIS) platform provides a unique opportunity to analyse extensive, real-world clinical data. This project will leverage CRIS and advanced natural language processing (NLP) techniques to extract and analyse structured data from free-text clinical notes, allowing for an in-depth examination of recorded disclosures of violence.

Using quantitative methodologies, this study will explore differences in violence disclosure rates, patient characteristics (e.g., age, gender, and ethnicity), and associated clinical diagnosis and symptomatology across remote and face-to-face consultations in mental health care consultations. Patterns of healthcare utilisation, referral pathways, and clinical outcomes following disclosure (i.e. documented recorded violence in electronic health records) will be investigated to assess potential disparities and service responses.

By providing a robust and data-driven evaluation of disclosure patterns, this project will offer valuable insights into the role of consultation modality in supporting individuals affected by violence. The findings have the potential to inform clinical practice, improve healthcare accessibility, and shape policy recommendations for optimising violence disclosure and response in both remote and in-person settings.

The student will work with a dataset derived from electronic health records (EHRs) to investigate documented recorded violence in electronic health records in remote versus face-to-face consultations. They will be supported to conduct and lead analyses assessing: (1) the prevalence and patterns of violence disclosure across different consultation modalities, (2) associations between disclosure and patient characteristics such as age, gender, and ethnicity, and (3) the impact of violence disclosure on healthcare utilisation and clinical outcomes.

The student will be supervised in applying advanced quantitative methodologies, including multivariate analysis and natural language processing (NLP) techniques, to extract and analyse structured data from clinical text. They will also be guided in interpreting findings to inform clinical practice and policy recommendations.

Students will gain practical experience in working with electronic health records, using large real- world big datasets and applying advanced quantitative methods.
Students will have the opportunity to cultivate expertise in the emerging field of mental health data science, gaining hands-on experience and skills in analysing and interpreting complex mental health data.
Students will work under the supervision of experienced researchers and gain valuable exposure to the work conducted by the CRIS team at the NIHR Maudsley Biomedical Research Centre.

Previous experience in statistical analysis.

The student will be encouraged and supported to lead/contribute to the writing of one academic paper on the main findings and will be encouraged to present the findings to one of our service user advisory groups, at academic seminars (e.g., regular departmental and/or CRIS seminars) and at academic conferences

1. Please send your CV and a brief cover letter outlining your interest and suitability to the project supervisor(s). Please contact the project supervisor(s) in advance of submitting the application with any questions.

2. If selected by the project supervisor

• LISS DTP students must then complete the LISS DTP Placement /Internship Application form. This ensures that there is approval of PhD supervisor, and the necessary information is obtained to extend funding (for DTP1 students) or confirm placement requirement fulfilled (for DTP2 students), and to fulfil ESRC reporting obligations. LISS DTP approval must be given before the RA internship can commence.
• Other ESRC-funded DTP students should follow the internship application processes from their home DTP.

Please note for LISS DTP students:

• Research Assistant Internships must not be undertaken with the student’s current supervisor and/or home department.
• DTP1 students (those whose funding commenced before Oct24): a maximum of 4 Research Assistant internships will be funded. These will be filled on a first-come, first-served basis. Once the 4 DTP1 places are filled, we will inform PIs that only DTP2 students are eligible for the Research Assistant internships. PLEASE NOTE THAT ALL DTP1 PLACES HAVE NOW BEEN FILLED.
• DTP2 students (those whose funding commenced from Oct24): are required to complete a 3-month placement, which is funded through their studentship. No limits to number that can be funded.
• Reports: at the conclusion of the internship, the student will be required to complete an internship report, which will include a question for the internship host to feedback on the internship.

Contact liss-dtp@kcl.ac.uk with any questions.