ArticleEuropean archives of psychiatry and clinical neuroscience2026
Psychometric validation of research domain criteria, general health, and clinical-psychosocial parts of the minimum dataset of the German Center for Mental Health-informing a Delphi process.
Article in European archives of psychiatry and clinical neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis study evaluated the latent structure and conceptual validity of the Research Domain Criteria (RDoC), General Health, and Clinical/Psychosocial clusters of the German Center for Mental Health (DZPG) Minimum Data Set (MDS). It aimed to determine whether these clusters represent interpretable constructs and demonstrate cross-group comparability across clinical and non-clinical populations, providing a data-driven basis for item selection within a Delphi consensus process.
methodsTwo subsamples were analyzed: a representative sample (N = 2,287) and a clinic-recruited sample (N = 322) from six DZPG sites. Multi-group confirmatory factor analyses tested the latent structure and assessed configural, metric, and scalar measurement invariance between non-clinical vs. clinical/help-seeking groups.
resultsAll three MDS clusters demonstrated excellent baseline model fit (RDoC: CFI = .990, RMSEA = .037; General Health: CFI = .993, RMSEA = .037; Clinical/Psychosocial factors: CFI = .992, RMSEA = .026). Analyses confirmed configural and scalar invariance across all clusters but indicated non-invariance at the metric level for the General Health cluster.
conclusionsThe results provide initial evidence for the structural validity and cross-group comparability of the RDoC and Clinical/Psychosocial clusters, while indicating limitations regarding metric non-invariance within the General Health cluster. They informed a Delphi consensus process and support the MDS as a scalable framework for diverse populations, particularly relevant for the DZPG's effort to harmonize mental health data across institutions and contexts.
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