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A proposed model for the psychometric evaluation of clinical case formulations with quantified causal diagrams.
Psychological Assessment ( IF 6.083 ) Pub Date : 2020-06-01 , DOI: 10.1037/pas0000811
Stephen N Haynes 1 , William H O'Brien 2 , Antonio Godoy 3
Affiliation  

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).

中文翻译:

拟议的模型与量化的因果关系图心理评估临床案例模型。

关于服务对象的行为问题和治疗目标的判断以及影响他们的因素,是大多数临床病例表述(CCF)的要素。这些判断旨在指导临床医生选择最有效的干预灶。尽管它们很重要,但CCF却很少进行心理测量评估。我们用量化的因果图描述了一个模型来促进和促进CCF的心理测评。本文介绍了量化因果图的概念基础,路径分析,好处和局限性。我们首先介绍适用于CCF和心理病理学的因果关系和因果图的概念。我们建议临床病例表述因果关系图可以加强基于科学的临床评估方法,促进CCF的心理测量评估,提高临床医生判断的特异性,准确性和可交流性,帮助临床医生选择最有效的干预重点,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)。
更新日期:2020-06-01
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