Building recovery and resilience in severe mental illness: Leveraging the role of social determinants in illness trajectories and interventions
Lead Research Organisation:
Queen Mary University of London
Department Name: Wolfson Institute of Population Health
Abstract
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Technical Summary
Severe Mental Illnesses (SMIs) arise from a complex interplay of biological, psychological, and social factors. Yet most frontline treatments for SMIs target biological and psychological factors. This hub will focus on "social determinants": the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems that shape the conditions of daily life. We will draw on multi-disciplinary and cross-sectoral expertise and experience, interweaving these insights with lived experiences to a) generate new knowledge on the role of social determinants in influencing the course and outcomes of SMIs and b) explore how we can leverage "protective" social factors to build resilience and recovery in people with SMIs.
Three workstreams are planned.
Workstream 1 will use machine learning predictive models to analyse longitudinal, secondary datasets containing data on individuals with various SMIs. We will explore how locality-based predictors of structural social determinants cluster and interact to predict the course and outcomes of various SMIs.
Workstream 2 will collect new information from a cohort of patients with SMIs to measure how the quantity and quality of interpersonal exchanges, relationships and support networks across social spheres contributes to the recovery journeys of people with SMIs. We will explore the biological and cognitive mechanisms by which social factors exert their effects.
Workstream 3 will explore the potential of social prescribing and community organising in supporting patients with SMIs. Creative workshops and sandpits with lived experience participants, community organisations, and other stakeholders will allow participants to share their experiences to shape the development of these protocols/programmes.
Addressing key social determinants of SMIs tackles health inequalities invites an opportunity for policy change to collectively support people with SMIs.
Three workstreams are planned.
Workstream 1 will use machine learning predictive models to analyse longitudinal, secondary datasets containing data on individuals with various SMIs. We will explore how locality-based predictors of structural social determinants cluster and interact to predict the course and outcomes of various SMIs.
Workstream 2 will collect new information from a cohort of patients with SMIs to measure how the quantity and quality of interpersonal exchanges, relationships and support networks across social spheres contributes to the recovery journeys of people with SMIs. We will explore the biological and cognitive mechanisms by which social factors exert their effects.
Workstream 3 will explore the potential of social prescribing and community organising in supporting patients with SMIs. Creative workshops and sandpits with lived experience participants, community organisations, and other stakeholders will allow participants to share their experiences to shape the development of these protocols/programmes.
Addressing key social determinants of SMIs tackles health inequalities invites an opportunity for policy change to collectively support people with SMIs.
