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Beyond linear mediation: Toward a dynamic network approach to study treatment processes

期刊

CLINICAL PSYCHOLOGY REVIEW
卷 76, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cpr.2020.101824

关键词

Therapy; Process; Treatment; Mediation; Complex Network; Dynamic System

资金

  1. Alexander von Humboldt Foundation
  2. NIH/NCCIH [R44AT006952, R01 AT007257]
  3. NIH/NIMH [R01MH099021, U01MH108168]
  4. James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition Special Initiative

向作者/读者索取更多资源

Few clinical scientists would disagree that more research is needed on the underlying mechanisms and processes of change in psychological therapies. In the dominant current approach, processes of change are studied through mediation. The study of mediation has been largely structured around a distinction between moderation and mediation first popularized by Baron and Kenny's (1986) seminal article, which is based on a nomothetic and cross-sectional framework. In this article, we argue that this approach is unable to adequately address change processes in psychological therapies, because it falsely assumes that treatment change is a linear, unidirectional, pauci-variate process and that the statistical assumptions are met to study processes of change in an individual using a nomothetic approach. In contrast, we propose that treatment is a dynamic process involving numerous variables that may form bi-directional and complex relationships that differ between individuals. Such relationships can best be studied using an individual dynamic network approach connected to nomothetic generalization methods that are based on a firm idiographic foundation. We argue that our proposal is available, viable, and can readily be integrated into existing research strategies. We further argue that adopting an individual dynamic network approach combined with experimental analyses will accelerate the study of treatment change processes, which is necessary as the field of evidence-based care moves toward a process-based model. We encourage future research to gather empirical evidence to examine this approach.

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