Schiffman Jason, Ellman Lauren M, Mittal Vijay A
Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, United States.
Department of Psychology, Temple University, Philadelphia, PA, United States.
Front Psychiatry. 2019 Feb 14;10:6. doi: 10.3389/fpsyt.2019.00006. eCollection 2019.
Approaches to identifying individuals at clinical high-risk (CHR) for psychosis currently do not carefully weigh considerations around individual differences. Effective identification depends on awareness of factors beyond psychopathology as it is reflected in the current literature, such as sensitivity to idiographic circumstances and individual differences. The inability to address contextual factors when employing the status quo method of identification likely contributes to the unacceptably poor accuracy when identifying people at CHR. Individual differences related to factors such as culture, race, comorbidity, and development likely play an important role in accurate identification, and have the potential to improve the validity of approaches intended to identify this population. Tailored approaches to assessment based on an awareness of context, identity, setting, and preferences of clients are possible, and customizing assessment efforts accordingly may be useful for accurate identification of people at CHR. Highlighting the potential for the existing early identification paradigm to marginalize or misunderstand certain groups, we describe how effective identification and ethical diagnosis require sensitivity to individual differences writ large. We suggest that recognizing the importance of these factors advances a more inclusive and accurate approach to identification.
目前,识别临床高危精神病个体的方法并未仔细权衡个体差异相关的因素。有效的识别取决于对当前文献中所反映的精神病理学之外的因素的认识,例如对独特情况和个体差异的敏感性。在采用现行识别方法时无法考虑背景因素,这可能是导致识别临床高危个体时准确性差得令人无法接受的原因。与文化、种族、共病和发育等因素相关的个体差异可能在准确识别中发挥重要作用,并且有可能提高旨在识别该人群的方法的有效性。基于对客户背景、身份、环境和偏好的认识,采用量身定制的评估方法是可行的,相应地定制评估工作可能有助于准确识别临床高危个体。我们强调了现有的早期识别模式可能会使某些群体边缘化或产生误解的可能性,描述了有效的识别和符合伦理的诊断如何需要对广义的个体差异保持敏感。我们建议,认识到这些因素的重要性有助于推进一种更具包容性和准确性的识别方法。