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Dez Kyte

Professor at School of Health and Wellbeing

University of Worcester

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United Kingdom

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Research Interests

Statistics

10%

Artificial Intelligence

10%

Psychology

10%

Neurodiversity

10%

Salud Pública

10%

Statistical Analysis

10%

Medical Science

10%

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Positions1

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Dez Kyte

University Name
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University of Worcester

PhD Studentship: AI-Assisted Multimodal and Multilingual ePROMs for Accessible Mental Health Care in Rural Populations with Severe Mental Illness

The University of Worcester invites applications for a full-time PhD studentship on the AIM Study: AI-Assisted Multimodal and Multilingual ePROMs for Accessible Mental Health Care in Rural Populations with Severe Mental Illness. This project addresses critical mental health inequalities in rural communities, where language diversity, low literacy, limited digital skills, and unreliable connectivity pose significant barriers to care. Individuals with severe mental illness (SMI) often lack access to validated electronic Patient-Reported-Outcome Measures (ePROMs), which are vital for early detection and suicide prevention. Traditional translation and cultural validation of PROMs can be slow and costly, limiting their availability in minority languages and failing to address accessibility for those with reading, comprehension, or digital interface challenges. Advances in artificial intelligence (AI) and large language models (LLMs) offer new opportunities for multilingual translation and adaptive, multimodal communication—such as speech, simplified text, pictorial explanations, and context-sensitive wording. These innovations can reduce literacy demands, support neurodiverse users, and enable engagement with PROMs even in low-connectivity rural environments. The project aims to fundamentally redesign accessibility in mental health care, supporting equity for rural populations. The successful applicant will collaborate with local stakeholders and individuals with lived experience to co-produce research aligned with the THRIVE People and Community Engagement and Involvement Strategy (PCIE). Key objectives include testing measurement equivalence between AI-assisted dynamic translations and traditional PROMs for SMI, assessing patient preferences and usability of AI-driven interfaces, exploring feasibility of on-device fine-tuning of small LLMs for personalized features (e.g., speech-based completion, plain language simplification, tone adjustments, offline functioning), and identifying barriers and facilitators for implementation in outpatient and home settings. Supervision will be provided by Professor Dez Kyte (Professor of Physiotherapy), Dr Chris Bowers (Principal Lecturer in Computing), and Professor Elizabeth Hughes (Professor of Mental Health Inequalities), all at the University of Worcester. The project is part of the NIHR THRIVE Programme and the newly established Rural Mental Health Research Unit, with opportunities for collaboration with the Institute for Mental Health at the University of Birmingham. The studentship is offered for 4 years full-time, including a tax-free bursary of £20,407 for 3 years, a fee-waiver for 4 years, a project budget, laptop and IT equipment, and access to Doctoral School facilities. Applicants are expected to have a Masters in Health Care or equivalent professional experience, and a First or Upper Second Honours Degree. The University welcomes applications from candidates with relevant professional qualifications and work-related experience as part of its commitment to widening participation. Research students at Worcester benefit from a comprehensive Researcher Development Programme, student-led conferences and seminars, and dedicated support from the Doctoral School. The closing date for applications is Friday, 8th May 2026. To apply, visit the University of Worcester webpage and click ‘apply now’ next to the project. For informal discussion, contact Professor Dez Kyte at [email protected]. References supporting the project include recent studies on rural mental health barriers, digital therapeutics, and the use of large language models in patient-reported outcome measures.

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