Department of Psychology.
Department of Philosophy.
Psychol Assess. 2021 May;33(5):452-458. doi: 10.1037/pas0000990. Epub 2021 Feb 11.
Clinical case formulations (CCFs) can be organized and communicated in several ways but one of the most effective is through CCF causal diagrams (CCFCDs). Haynes et al., , 2020, 32, 541 illustrated how the psychometric evaluation of CCFCDs could be facilitated by assigning quantitative values to the clinician's judgments in a CCF. Although quantification could facilitate the psychometric evaluation CCFCDs, it is less clear that it can help clinicians make decisions about the best treatment foci. This article presents an open-source computer program ( CCFCDC) for the path analyses of quantified CCFCDs, based on the free computing language Python, to assist in clinical decision making. The operation, examples, assets, and limitations of the CCFCDC are discussed in the context of measurement principles, precision, and uncertainty in clinical judgments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
临床病例表述（CCFs）可以通过多种方式进行组织和交流，但其中最有效的方法之一是通过 CCF 因果图（CCFCD）。Haynes 等人，2020 年，32 卷，541 页，说明了如何通过为 CCF 中的临床医生判断分配定量值来促进 CCFCD 的心理计量评估。虽然量化可以促进 CCFCD 的心理计量评估，但尚不清楚它是否可以帮助临床医生做出关于最佳治疗焦点的决策。本文介绍了一个基于免费计算语言 Python 的用于量化 CCFCD 路径分析的开源计算机程序（CCFCDC），以协助临床决策。在测量原则、临床判断的精度和不确定性的背景下，讨论了 CCFCDC 的操作、示例、资产和局限性。（PsycInfo 数据库记录（c）2021 APA，保留所有权利）。
