Trajectories of change among highly challenging patients in intensive long‐term psychoanalytic psychotherapy.
Published In: Journal of Clinical Psychology, 2023, v. 79, n. 11. P. 2529 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Yonatan‐Leus, Refael; Abargil, Maayan; Shefler, Gaby; Finkenberg, Ron; Amir, Ilan 3 of 3
Abstract
Objectives: This study aimed to identify and describe trajectories of change in distress among highly challenging patients who had received long and intensive psychoanalytic psychotherapy. Methods: The longitudinal version of the K‐means algorithm was applied to the outcome measures data of 74 patients treated in four public mental health centers. The patients were measured five times at 6‐month intervals for three outcome measures. Results: For the OQ45 and Symptom Checklist‐90, one trajectory was marked by a lower initial distress level. In this trajectory, the improvement occurred in the first half of the measurements, with a plateau thereafter. A second trajectory was characterized by higher initial severity and an improvement, mainly in the second part of the measurements. For the Beck Depression Inventory, one trajectory was marked by lower initial distress. In this group, the improvement occurred throughout the entire period. The remaining patients were characterized by higher initial distress and a decreased level of distress in the last part of treatment. They began to improve only during the third year of therapy. Conclusion: The response to treatment is not uniform in long‐term treatment for highly challenging patients. A significant number of patients require a longer period of therapy to ignite improvement. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Clinical Psychology. 2023/11, Vol. 79, Issue 11, p2529
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0021-9762
- DOI:10.1002/jclp.23560
- Accession Number:172913643
- Copyright Statement:Copyright of Journal of Clinical Psychology is the property of Wiley-Blackwell 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.