Mapping mental ill-health and health within a large, representative UK school-based sample of adolescents

To support effective healthcare, frameworks are needed that capture the real-world complexity of youth mental health. We applied unsupervised machine learning (Growing Hierarchical Self-Organizing Map; GHSOM) to data from a representative sample of over 26,000 adolescents (11-14 years) from 85 UK schools. Input features included anxiety, depression, conduct, hyperactivity, interpersonal challenges, and markers of mental well-being. GHSOM represents mental health hierarchically, from narrow to broader profiles and, at the highest hierarchical level, eight reproducible profiles were identified. Profiles ranged from flourishing adolescents to those with widespread social-emotional and behavioural difficulties and low mental well-being. Other profiles showed moderate well-being despite minimal symptoms, or focal patterns of internalising or externalising. Regarding sex, females were more likely to experience patterns of anxiety and depression, while males displayed more depression, behavioural, and socialization challenges. Overall, this study offers a data-driven, multidimensional framework for organising youth mental health and informing intervention and healthcare.