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区分治疗效果不佳和良好的心理治疗的因素是什么?一种受统计力学启发的心理治疗研究方法,第二部分:网络分析。

What Differentiates Poor- and Good-Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research, Part Two: Network Analyses.

作者信息

de Felice Giulio, Giuliani Alessandro, Gelo Omar C G, Mergenthaler Erhard, De Smet Melissa M, Meganck Reitske, Paoloni Giulia, Andreassi Silvia, Schiepek Guenter K, Scozzari Andrea, Orsucci Franco F

机构信息

Department of Dynamic and Clinical Psychology, Sapienza University of Rome, Rome, Italy.

Department of Psychology, NCIUL University, London, United Kingdom.

出版信息

Front Psychol. 2020 May 20;11:788. doi: 10.3389/fpsyg.2020.00788. eCollection 2020.

Abstract

Statistical mechanics is the field of physics focusing on the prediction of the behavior of a given system by means of statistical properties of ensembles of its microscopic elements. The authors examined the possibility of applying such an approach to psychotherapy research with the aim of investigating (a) the possibility of predicting good and poor outcomes of psychotherapy on the sole basis of the correlation pattern among their descriptors and (b) the analogies and differences between the processes of good- and poor-outcome cases. This work extends the results reported in a previous paper and is based on higher-order statistics stemming from a complex network approach. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and transcripts of the sessions were coded according to Mergenthaler's Therapeutic Cycle Model (TCM), i.e., in terms of abstract language, positive emotional language, and negative emotional language. The relative frequencies of the three vocabularies in each word-block of 150 words were investigated and compared in order to understand similarities and peculiarities between poor-outcome and good-outcome cases. Network analyses were performed by means of a cluster analysis over the sequence of TCM categories. The network analyses revealed that the linguistic patterns of the four good-outcome and four poor-outcome cases were grounded on a very similar dynamic process substantially dependent on the relative frequency of the states in which the transition started and ended ("random-walk-like behavior", adjusted = 0.729, < 0.001). Furthermore, the psychotherapy processes revealed statistically significant changes in the relative occurrence of visited states between the beginning and the end of therapy, thus pointing to the non-stationarity of the analyzed processes. The present study showed not only how to quantitatively describe psychotherapy as a network, but also found out the main principles on which its evolution is based. The mind, from a linguistic perspective, seems to work-through psychotherapy sessions by passing from the most adjacent states and the most occurring ones. This finding can represent a fertile ground to rethink pivotal clinical concepts such as the timing of an interpretation or a comment, the clinical issue to address within a given session, and the general task of a psychotherapist: from someone who delivers a given technique toward a consultant promoting the flexibility of the clinical field and, thus, of the patient's mind.

摘要

统计力学是物理学的一个领域,专注于通过给定系统微观元素系综的统计特性来预测该系统的行为。作者研究了将这种方法应用于心理治疗研究的可能性,目的是探究:(a)仅根据心理治疗描述符之间的相关模式来预测心理治疗良好和不良结果的可能性;(b)良好和不良结果案例过程之间的异同。这项工作扩展了先前一篇论文中报告的结果,并且基于源自复杂网络方法的高阶统计量。记录了四个良好结果和四个不良结果的简短心理治疗案例,并根据默根塔勒的治疗循环模型(TCM)对会话记录进行编码,即从抽象语言、积极情绪语言和消极情绪语言的角度进行编码。研究并比较了每个150字单词块中三种词汇的相对频率,以了解不良结果和良好结果案例之间的异同。通过对TCM类别序列进行聚类分析来进行网络分析。网络分析表明,四个良好结果和四个不良结果案例的语言模式基于一个非常相似的动态过程,该过程在很大程度上取决于转变开始和结束状态的相对频率(“类随机游走行为”,调整后 = 0.729,< 0.001)。此外,心理治疗过程显示,治疗开始和结束之间所访问状态的相对出现频率存在统计学上的显著变化,从而表明所分析过程的非平稳性。本研究不仅展示了如何将心理治疗定量描述为一个网络,还找出了其演变所基于的主要原则。从语言角度来看,心灵似乎通过从最相邻和最常出现的状态经过心理治疗会话来发挥作用。这一发现可能为重新思考关键的临床概念提供丰富的素材,比如解释或评论的时机、给定会话中要处理的临床问题以及心理治疗师的一般任务:从实施特定技术的人转变为促进临床领域灵活性从而促进患者思维灵活性的顾问。

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