Purpose
Mental well-being is a cornerstone of recovery for people with mental disorders. Unfortunately, despite many studies on the topic, the literature still lacks results from large samples processed using advanced models capable of taking numerous variables into account. This study aims to leverage robust machine learning techniques on data from the REHABase cohort to identify key predictors of mental well-being in patients with severe mental disorders, with a particular focus on schizophrenia.
Methods
In all, 47 clinical, psychological, and sociodemographic variables were selected. Three distinct samples were analyzed: all patients (n = 2206), schizophrenia patients (n = 1136) and non-schizophrenia patients (n = 1070). The performances of 3 machine learning algorithms were compared: regularized linear regression, random forest, and gradient boosting with tenfold cross-validation.
Results
The regularized linear regression model was found to be the best performing in terms of generalizability. Results highlighted the influence of the internalized stigmatization subscales of resistance, resilience, and alienation alongside the resilience dimension of quality of life in predicting mental well-being across all of the disorders. For schizophrenia patients, mental well-being was primarily predicted by social withdrawal.
Conclusion
Using advanced machine learning methodology on a large sample of patients with transdiagnostic exploration, our results showed both the crucial role of resilience and resistance to stigma in overall mental well-being and the critical importance of social contact in schizophrenia. These insights should help improve care trajectories for individuals with severe psychopathologies, particularly by promoting social cognitive remediation in schizophrenia.
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