2026 entry King’s MRC DTP.

Project ID - NS-MH2026_27

You can access further details of the project on the MRC DTP website.

Check out more details about the MRC DTP prorgramme, including application timelines.

Modelling Student Mental Health in the Higher Education context: data linkage to identify systemic risk and protective factors in a complex system.

Mental health problems affect around 40% of young people, making this a pressing public health concern. While university students face many of the same challenges as their non-student peers, they are also exposed to distinct academic and institutional pressures. UK universities are now expected to adopt a “whole university” approach—addressing not only individual vulnerabilities but also structural and cultural drivers of poor mental health.

This interdisciplinary project offers a unique opportunity to link rich individual-level mental health survey data with institutional data. This allows for analysis not only of how student mental health changes over time, but also how environmental and cultural factors within universities shape these trajectories. The approach brings a much-needed public health lens to student mental health, supporting early identification of risk, understanding contextual influences, and informing systemic, preventative solutions.

Drawing on expertise from Psychology and Physics, we will apply mathematical models to explore how changes in university structures could improve student wellbeing. Working with King’s Student Services, the student will use linked data from the King’s Wellbeing Survey (c. 4,000 students annually; 2024–2027) to generate findings that directly inform university policy and service design.

The project takes a mixed-methods approach. Alongside quantitative modelling (e.g. multilevel and longitudinal analysis, clustering), the student will co-produce findings with a student steering group and conduct qualitative interviews to explore the lived experience behind the data.

The student will gain expertise in data analysis, systems modelling, stakeholder engagement, qualitative research, and knowledge mobilisation. They will work closely with institutional teams, gaining valuable experience in applied research that shapes real-world decision-making.

Aim:
To identify how individual and institutional factors interact to influence student mental health trajectories, develop a model of the university ecosystem, and co-produce actionable insights for the sector.

PhD Timeline:

Year 1: Curate datasets; build relationships with stakeholders and a student group; complete MRes (if applicable).

Year 2: Conduct initial analyses; design and carry out interviews.

Year 3: Analyse trajectories and subgroup differences; apply clustering and begin modelling the university ecosystem.

Year 4: Finalise model; integrate findings; write thesis.

Rotation project (3 months):
Explore how faculty-level indicators of academic culture (e.g. extension requests, support-for-study cases) cluster across faculties and relate to student wellbeing. Involves data integration, descriptive statistics, and stakeholder consultation.