COPS在行动:探索青少年心理治疗MATCH的使用结构

Psych Pub Date : 2023-04-19 DOI:10.3390/psych5020020
T. Rusch, Katherine E. Venturo-Conerly, Gioia Baja, P. Mair
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引用次数: 1

摘要

本文介绍了针对从业者的集群优化邻近缩放(COPS),以及关于相应R包COP使用的教程。COPS是多维缩放(MDS)的一种变体,其目的是提供聚类配置,同时仍然忠实地表示多元相异性。它包含了最流行的MDS版本作为特殊情况。我们用临床心理学的数据说明了该方法和包的思想、用途、灵活性和多功能性,这些数据说明了临床医生如何在野外使用儿童模块化治疗方法(MATCH)的模块。我们在原始空间中用基于密度的分层聚类和用支持向量机进行面对面分析来补充COPS分析。我们发现,使用COPS进行缩放可以提供模块的合理和深入的空间排列,允许轻松识别模块集群,并提供与MATCH协议相对应的模块的清晰方面。在这方面,COPS比标准MDS和集群都工作得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COPS in Action: Exploring Structure in the Usage of the Youth Psychotherapy MATCH
This article is an introduction to Cluster Optimized Proximity Scaling (COPS) aimed at practitioners, as well as a tutorial on the usage of the corresponding R package cops. COPS is a variant of multidimensional scaling (MDS) that aims at providing a clustered configuration while still representing multivariate dissimilarities faithfully. It subsumes most popular MDS versions as special cases. We illustrate the ideas, use, flexibility and versatility of the method and the package with data from clinical psychology on how modules of the Modular Approach to Therapy for Children (MATCH) are used by clinicians in the wild. We supplement the COPS analyses with density-based hierarchical clustering in the original space and faceting with support vector machines. We find that scaling with COPS gives a sensible and insightful spatial arrangement of the modules, allows easy identification of clusters of modules and provides clear facets of modules corresponding to the MATCH protocols. In that respect COPS works better than both standard MDS and clustering.
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