Measuring Mental Health Variables in Computational Research: Toward Validated, Dimensional, and Transdiagnostic Approaches
This addresses validity issues in computational mental health research for practitioners, but it is incremental as it focuses on methodological recommendations rather than new breakthroughs.
The paper tackles the problem of inappropriate measures of psychopathology in computational mental health research, identifying reliance on unvalidated, categorical, and disorder-specific measures, and recommends using validated, dimensional, and transdiagnostic approaches to improve validity.
Computational mental health research develops models to predict and understand psychological phenomena, but often relies on inappropriate measures of psychopathology constructs, undermining validity. We identify three key issues: (1) reliance on unvalidated measures (e.g., self-declared diagnosis) over validated ones (e.g., diagnosis by clinician); (2) treating mental health constructs as categorical rather than dimensional; and (3) focusing on disorder-specific constructs instead of transdiagnostic ones. We outline the benefits of using validated, dimensional, and transdiagnostic measures and offer practical recommendations for practitioners. Using valid measures that reflect the nature and structure of psychopathology is essential for computational mental health research.