计算研究中精神健康变量的测量：迈向经过验证的、维度的和跨诊断的方法
Measuring Mental Health Variables in Computational Research: Toward Validated, Dimensional, and Transdiagnostic Approaches
AI总结：
本文指出计算精神健康研究常使用不当测量，提出采用经过验证、维度性和跨诊断的测量方法，以提升研究有效性并给出实践建议。
摘要：
计算精神健康研究开发模型来预测和理解心理现象，但往往依赖于不恰当的精神病理学构念测量，这削弱了有效性。我们识别出三个关键问题：（1）依赖未经验证的测量（例如，自我声明的诊断）而非经过验证的测量（例如，临床医生的诊断）；（2）将精神健康构念视为类别性的而非维度性的；以及（3）关注特定于障碍的构念而非跨诊断的构念。我们概述了使用经过验证的、维度的和跨诊断的测量的好处，并为从业者提供实用建议。使用反映精神病理学本质和结构的有效测量对于计算精神健康研究至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
