ŘIHÁČEK, Tomáš, Robert ELLIOTT and Jesse OWEN. The structure and dynamics of client session reactions: A longitudinal network analysis. In 53nd Annual International Meeting of the Society for Psychotherapy Research. 2022.
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Basic information
Original name The structure and dynamics of client session reactions: A longitudinal network analysis
Authors ŘIHÁČEK, Tomáš, Robert ELLIOTT and Jesse OWEN.
Edition 53nd Annual International Meeting of the Society for Psychotherapy Research, 2022.
Other information
Original language English
Type of outcome Presentations at conferences
Field of Study 50100 5.1 Psychology and cognitive sciences
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Organization unit Faculty of Social Studies
Keywords in English Client session reactions; longitudinal network analysis
Tags International impact, Reviewed
Changed by Changed by: prof. Mgr. Tomáš Řiháček, Ph.D., učo 21252. Changed: 1/8/2022 09:55.
Abstract
Objective: Understanding complex relationships among various aspects of clients’ session experience is essential for effective therapy. This study aimed to test the feasibility of applying longitudinal network modeling to understand the structure and dynamics of clients’ session reactions. Method: Data from three samples were combined (two general outpatient samples of emotion-focused therapy (EFT) and one study comparing EFT and cognitive-behavioral therapy for trauma). One hundred and twenty-three psychotherapy clients answered the Revised Session Reactions Scale after every session. Data collected after sessions 2 to 5 (413 observations) were used to fit a lag-1 dynamic latent variable model for panel data. The temporal, contemporaneous, and between-person networks were obtained and analyzed exploratively. Data from session 1 and sessions 6+ could not be used due to the nonstationarity of model parameters and low sample size. Results: Session reactions that played the most central role in the temporal prediction of other reactions included distancing from one’s thoughts, feelings, or memories and new discoveries related to the self. Both types of experience predicted, among others, the therapeutic relationship and a sense of relief. Feeling involved in therapy had a marginal role in the network. Conclusions: Longitudinal network modeling proved to be a promising approach to exploring clients’ session experience and, in a broader sense, to the analysis of psychotherapy process data. It yielded innovative and clinically meaningful findings of the interrelatedness of complex session-by-session processes.
Links
TL03000049, research and development projectName: Deep learning v psychoterapii: Strojová analýza nahrávek terapeutických sezení (Acronym: DeePsy)
Investor: Technology Agency of the Czech Republic
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