| Project Supervisor | Helen (Lenny) Buxton |
| Institution & Department | Queen Mary University of London – Centre for Psychiatry and Mental Health, Wolfson Institute of Public Health |
| Research Area | RA1: Global Health Innovation |
| Project Start Date | End of June 2026 |
| Project Duration | 3 months |
| Application Deadline | 4th June 2026 |
| Working Pattern | Part-time (2.5 days per week over 6 months) |
| Working Arrangements | Hybrid |
| Ideally the intern will be in the office on Mondays. The office is at the Yvonne Carter Building, Whitechapel. If the intern wishes they can work from the office through the week. Remote working arrangements can be discussed. | |
| How to Apply | View Guidance Here |
Project Description
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This project will use existing data from the Social Health Cohort Study, a large-scale study investigating social connectedness and participant mental health outcomes. The Social Health Cohort Study is a core research output of the Social Health Hub – a critical pillar of the UKRI funded Mental Health Platform. The Social Health Hub is a five-year research programme exploring how social determinants, the conditions in which people are born, grow, work, live, and age influence the course of severe mental illness (SMI), and shape how people respond to their conditions overtime. Our overall goal is to develop the evidence base for how to leverage social resources to deliver benefits that go beyond medication or therapy alone.
The Social Health Cohort Study is recruiting 600 participants with SMI from NHS mental health services. The study will investigate how the size, composition and quality of personal social networks predict changes in quality of life, daily functioning and symptom severity over a 12-month period. As part of the study we are using Ecological Momentary Assessment (EMA) methods to understand how social interactions, activities and locations interact with mental health outcomes in the moment and across the course of the day. EMA utilises smartphone-based prompts to collect in-depth in the moment response as the participant goes about their day to day life. This approach enables us as researchers to get out of the clinic and investigate in-depth how mood, thoughts and feelings fluctuate moment by moment.
In addition, EMA reduces recall bias so often associated with self-report measures. For the Social Health Cohort Study EMA participants will be asked to answer prompts sent direct to their mobile phone 6 times a day for 7 days. The technique generates many data points to interrogate both between person and within person change over time. By the time the student starts the internship we anticipate EMA data will be available for 100 + participants.
The student will be asked to develop and apply advanced statistical data analysis models which will best enable interpretation of these intensive longitudinal data, drawing on emerging methods within EMA literature. This work will contribute directly to analytical framework of the Social Health Hub, informing data analysis as the study progresses. The student will also be encouraged to disseminate findings from preliminary analysis, supporting both their own development and the wider research programme.
Internship Details
The student will work with Ecological Momentary Assessment (EMA) and cohort data from the Social Health Cohort Study. Their primary responsibility will be to develop and implement appropriate statistical models for analysing intensive longitudinal data, focusing on how daily social interactions, activities, and contexts are associated with momentary changes in mental health outcomes.
Specific activities will include: data cleaning and preparation of EMA datasets; exploration of data structure and missingness; and the development of advanced quantitative models (e.g. multilevel models or network-based approaches) to examine within-person and between-person effects over time. The student will be encouraged to engage with emerging methodological literature in EMA and tailor their analytical approach accordingly.
The student will be encouraged to attend training on multi-level modelling, attend regular supervision meetings and contribute to team discussions within the Social Health Hub and the Youth Resilience Unit more generally. The student will gain experience of collaborative and interdisciplinary research. They will also have the opportunity to present interim findings to the research team.
Intended outputs include: a fully documented and reproducible analysis pipeline, a set of statistical models suitable for ongoing use within the project, publication of data analysis plan to ISF.io, write up of preliminary findings suitable for publication to journal, development of conference abstract.
There will be opportunities to shadow other research activities in the Social Health Hub and/or the Youth Resilience Unit, as deemed beneficial and useful to the student. The Social Health Hub is led by Prof. Jennifer Lau in the Centre for Psychiatry and Mental Health (CPMH) at QMUL.
The student would be working with the team of inter-disciplinary researcher at universities across the UK. In addition they would benefit from being part of the supportive team at CPMH. The team comprises many early career researchers (RA, PhD and Post Doc level) working across a spectrum of mental health research, and the centre is set up to foster collaboration and shared learning.
Anticipated Benefits for the Student
The student will develop strong skills in advanced quantitative model development, and interpretation of data models. They will also be supported to develop their academic writing and presentation skills. In addition, they will develop a solid grounding in research on severe mental illness and in network analysis.
The student will be part of the team of researchers at the Social Health Hub, gaining exposure to inter-disciplinary approaches and findings which will provide solid grounding for their future career. The Hub is led by Prof Jennifer Lau at QMUL, and is a collaboration of 8 UK universities (KCL, Liverpool, Warwick, Plymouth, Newcastle, Brunel, City).
The student will also benefit from being part of the UKRI funded Mental Health Platform. They will be invited to attend the in-person research summit in October in Sheffield, providing opportunity to connect with and learn from a network of researchers from across the UK with the shared aim of accelerating research into severe mental illness.
In addition, the student will have access to specialised training opportunities offered at QMUL, and via the Mental Health Platform. All researchers working on the MHP have access to training offered by Oxford Health Biomedical Research Centre.
Skills, Experience and Knowledge Requirements
Essential Requirements:
- Knowledge of advanced quantitative methods and desire to specialise further
Desirable Requirements:
- Experience using R, experience and knowledge of statistical modelling techniques such as multi-level modelling, time series modelling, network modelling.