提出心理病理学研究应采用标准化、连续化和跨诊断的测量方法。
Measuring Mental Health Variables in Computational Research: Toward Validated, Dimensional, and Transdiagnostic Approaches
- 用临床诊断替代自报诊断，提升测量有效性
- 将心理问题视为连续维度而非二元分类
- 推荐跨诊断框架，适合多类心理障碍研究
计算心理卫生研究构建模型以预测和理解心理现象，但常依赖未经验证的测量方式（如自述诊断），损害研究有效性。我们指出三大问题：(1) 使用未验证的测量（如自述诊断）而非经过验证的方法（如临床诊断）；(2) 将心理健康问题视为类别而非连续维度；(3) 过度关注特定疾病构念，忽视跨诊断构念。本文阐明使用经验证、连续性和跨诊断测量的优势，并为研究者提供实践建议。采用反映心理病理本质与结构的有效测量，对计算心理卫生研究至关重要。
原文摘要 · Abstract (English)
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.
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