Department of Psychology, University of Hawai'i at Mānoa.
Department of Psychology, Bowling Green State University.
Psychol Assess. 2020 Jun;32(6):541-552. doi: 10.1037/pas0000811. Epub 2020 Mar 2.
Judgments about a client's behavior problems and treatment goals, and the factors that influence them, are elements of most clinical case formulations (CCFs). These judgments are designed to guide clinicians' selection of the most effective intervention foci. Despite their importance, CCFs have undergone infrequent psychometric evaluations. We describe a model to promote and facilitate the psychometric evaluation of CCFs with quantified causal diagrams. This article presents the conceptual foundations, path analyses, benefits, and limitations of quantified causal diagrams. We first present concepts of causality and causal diagrams that are applicable to CCF and psychopathology. We propose that clinical case formulations causal diagrams can strengthen a science-based approach to clinical assessment, facilitate the psychometric evaluation of CCFs, enhance the specificity, precision, and communicability of clinicians' judgments, help the clinician select the most effective intervention foci, predict the effects of changes in causal variables, and emphasize the importance of "uncertainty" in CCFs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
关于患者行为问题和治疗目标的判断，以及影响这些判断的因素，是大多数临床病例构成（CCFs）的要素。这些判断旨在指导临床医生选择最有效的干预焦点。尽管它们很重要，但 CCF 很少进行心理计量学评估。我们描述了一种使用量化因果图来促进和便利 CCF 心理计量学评估的模型。本文介绍了量化因果图的概念基础、路径分析、优点和局限性。我们首先介绍了适用于 CCF 和精神病理学的因果关系和因果图的概念。我们提出，临床病例构成因果图可以加强基于科学的临床评估方法，促进 CCF 的心理计量学评估，提高临床医生判断的特异性、精确性和可传达性，帮助临床医生选择最有效的干预焦点，预测因果变量变化的影响，并强调 CCF 中“不确定性”的重要性。（PsycInfo 数据库记录（c）2020 APA，保留所有权利）。
