This study provides an evaluation of group schema therapy (ST) for inpatient treatment of patients with personality pathology who did not respond to previous psychotherapeutic interventions. Forty-two patients were assessed pre- and posttreatment, and 35 patients were evaluated at follow-up 6 months later. The results showed a dropout rate of 35%. Those who dropped out did not differ from those who completed treatment with regard to demographic and clinical variables; the only exception was that those who dropped out showed a lower prevalence of mood disorders. Furthermore, intention-to-treat analyses showed a significant improvement in maladaptive schemas, schema modes, maladaptive coping styles, mental well-being, and psychological distress after treatment, and these improvements were maintained at follow-up. On the other hand, there was no significant change in experienced parenting style as self-reported by patients. Changes in schemas and schema modes measured from pre- to posttreatment were predictive of general psychological distress at follow-up. Overall, these preliminary findings suggest that positive treatment results can be obtained with group ST-based inpatient treatment for patients who did not respond to previous psychotherapeutic interventions. Moreover, these findings are comparable with treatment results for patients without such a nonresponsive treatment history. (PsycINFO Database Record
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http://dx.doi.org/10.1037/pst0000056 | DOI Listing |
Neuro Endocrinol Lett
December 2024
Department of Psychiatry, Faculty of Medicine and Dentistry, Palacky University in Olomouc, Czech Republic.
Objective: This article focuses on utilizing therapeutic letters within group schema therapy-an innovative therapeutic approach that integrates elements from various therapeutic disciplines. The primary aim is to explore how therapeutic letters can enhance the therapeutic process and support the treatment of patients.
Methods: To achieve this objective, we conducted a narrative literature review centred on schema therapy and using therapeutic letters as a therapeutic strategy.
Neuro Endocrinol Lett
December 2024
Department of Psychological Sciences, Faculty of Social Sciences and Health Care, Constantine the Philosopher University in Nitra, Slovak Republic, Czech Republic.
This article describes using imagery approaches during group schema therapy (GST). Imagery approaches are an important tool for identifying and changing maladaptive schema modes and early maladaptive schemas. It summarises the theoretical background of the group imagery method and practical case vignettes.
View Article and Find Full Text PDFNeural Netw
December 2024
College of Computer Science, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University, Hangzhou, 310027, China. Electronic address:
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar behaviors, while it may be violated in many real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs) have attracted increasing attention by modifying the neural message passing schema for heterophilous neighborhoods.
View Article and Find Full Text PDFSci Rep
December 2024
School of Marxism, China University of Political Science and Law (CUPL), Beijing, 100091, China.
To improve students' understanding of physical education teaching concepts and help teachers analyze students' cognitive patterns, the study proposes an association learning-based method for understanding physical education teaching concepts using deep learning algorithms, which extracts image features related to teaching concepts using convolutional neural networks. Moreover, a neurocognitive diagnostic model based on hypergraph convolution is constructed to mine the data of students' long-term learning sequences and identify students' cognitive outcomes. The findings revealed that the highest accuracy of the association graph convolutional neural network was 0.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Klinikum Stuttgart, Stuttgart Cancer Center - Tumorzentrum Eva Mayr-Stihl DE, Kriegsbergstraße 60, Stuttgart, D-70174, Germany.
Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.
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