JOURNAL ARTICLE

Microanalysis of Positive and Negative Content in Solution-Focused Brief Therapy and Cognitive Behavioral Therapy Expert Sessions.

  • Published In: Journal of Systemic Therapies, 2024, v. 43, n. 3. P. 50 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Jordan, Sara Smock; Froerer, Adam S.; Bavelas, Janet Beavin 3 of 3

Abstract

The models of cognitive behavioral therapy (CBT) and solution-focused brief therapy (SFBT) differ in their primary focus: problem solving versus solution building. These theoretical differences imply dissimilar practices, including the content of the therapeutic dialogue. Specifically, CBT sessions should include more talk about negative topics in clients' lives such as problems and situational difficulties, whereas SFBT sessions should focus on positive topics in clients' lives such as strengths and resources. We tested whether expert practice reflects these differences in the models. A reliable microanalysis revealed that demonstration sessions by three experts in each model differed significantly in the expected directions: negative content was significantly higher in CBT than SFBT sessions, and positive content was significantly higher in SFBT than CBT sessions. There was also a significant tendency for clients to respond in kind (i.e., negative therapist content was followed by negative client content, and positive therapist content by positive client content). [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Systemic Therapies. 2024/09, Vol. 43, Issue 3, p50
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:1195-4396
  • DOI:10.1521/jsyt.2024.43.3.50
  • Accession Number:185308621
  • Copyright Statement:Copyright of Journal of Systemic Therapies is the property of Guilford Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.