Identification of transdiagnostic phenomena among patients, the general population, relatives, and mental health professionals using topic modeling techniques

Alexis Vancappel^1*Hugo Kazzi²Hayfa Zgaya-Biau²Rodolphe Saur³Eva Fourel³Robert Courtois⁴Géraldine Tapia⁵Thierry Kosinski²Arnaud Carré⁶Catherine Bortolon⁷Fanny Marteau-Chasserieau⁸Lucia Romo⁹Celine Baeyens⁷Yannick Morvan⁹Chrystel Besche-Richard¹⁰Wissam El-Hage⁴

• ¹Université de Lille, Lille, France
• ²Universite de Lille, Lille, France
• ³Axiomavox, Paris, France
• ⁴Universite de Tours, Tours, France
• ⁵Universite de Bordeaux, Talence, France
• ⁶Universite Savoie Mont Blanc, Chambéry, France
• ⁷Universite Grenoble Alpes, Saint-Martin-d'Hères, France
• ⁸Ecole de Psychologues Praticiens, Paris, France
• ⁹Universite Paris Nanterre, Nanterre, France
• ¹⁰Universite de Reims Champagne-Ardenne, Reims, France

Introduction Recent research has highlighted the limitations of the categorical approach to mental disorders and has increasingly supported the development of a transdiagnostic perspective. This emerging approach focuses on common distal factors (circumstantial, biological, and social) and psychological processes that contribute to psychological suffering across a range of disorders, as well as on the resulting psychological symptoms. The present study aims to identify transdiagnostic distal factors, psychological processes, and symptoms by analyzing narratives through topic modeling—an unsupervised machine learning technique, specifically within Natural Language Processing (NLP). Topic modeling enables the automatic extraction of latent themes from unstructured text, making it possible to identify psychological patterns grounded in patients' lived experiences. Method We recruited four groups of participants: Patients diagnosed with a psychiatric disorder (N = 445), Individuals from the general population (N = 570), Relatives of patients with psychiatric disorders (N = 354), and Mental health professionals (N = 131). Participants answered open-ended questions exploring the causes of psychological suffering, their wishes for change, and their previous experiences with psychotherapy. Results We identified 258 topics, which were organized into 12 overarching themes. The most prominent topics concerned Emotional and Psychological Difficulties, Family and Social Relationships, and Therapeutic Processes. Each theme showed a comparable prevalence across the different diagnostic categories, supporting the transdiagnostic nature of these phenomena. Conclusion Topic modeling can be used effectively to identify transdiagnostic distal factors, psychological processes, and symptoms from diverse narratives. This approach tends to provide a novel means of supporting the relevance and validity of the transdiagnostic perspective.