Like other mental disorders, major depression is increasingly explained as a biomedical illness. We examined, in a treatment-seeking sample, whether attributing one's depression to biomedical causes would be associated with pessimistic psychotherapy treatment expectancies. Individuals seeking psychotherapy for depression rated their endorsement of biomedical explanations for their symptoms, expectations regarding treatment outcome, and expectations about forming a working alliance with a therapist. We found that treatment seekers' endorsement of biomedical explanations for their symptoms was associated with pessimism about treatment being successful. This pessimism was, in turn, associated with holding more negative expectancies about one's ability to form a strong therapeutic alliance with a therapist. Given the ascendancy of biomedical explanations for depression and the influence of patient expectancies on clinical outcomes, strategies for disassociating biomedical attributions from pessimistic expectancies may be needed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8333248 | PMC |
| http://dx.doi.org/10.1037/pst0000320 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Background: Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.
View Article and Find Full Text PDFA cost-reducing reimbursement programme? Effects of value-based reimbursement on healthcare costs.
Front Public Health
December 2024
Department of Health, Medicine and Caring Sciences (HMV), Linköping University, Linköping, Sweden.
Value-based reimbursement programmes have become increasingly common in attempts to bend the cost curve of healthcare without negative effects on quality. The aim of this study was to analyse the effect of introducing a value-based reimbursement programme on the cost to third-party payer. We performed a retrospective observational study with a before and after design based on the introduction of a value-based reimbursement programme in Sweden.
View Article and Find Full Text PDFSocial isolation profiles and conditional process analysis among postoperative enterostomy patients with colorectal cancer.
BMC Psychol
December 2024
School of Nursing, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Purpose: This study aimed to identify social isolation latent profiles and analyze the specific mechanisms in which social support, resilience, and posttraumatic growth associated social isolation from the perspective of positive psychology. Suggestions were offered to improve the mental health status of postoperative enterostomy patients with colorectal cancer.
Methods: This was a cross-sectional survey.
Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.
Eur J Nucl Med Mol Imaging
December 2024
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, 610041, China.
Purpose: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.
Methods: A total of 562 patients from four centers were divided into the training cohort, validation cohort and test cohort.
Identifying potential drug-drug interactions (DDIs) before clinical use is essential for patient safety yet remains a significant challenge in drug development. We presented DDI-GPT, a deep learning framework that predicts DDIs by combining knowledge graphs (KGs) and pre-trained large language models (LLMs), enabling early detection of potential drug interactions. We demonstrated that DDI-GPT outperforms current state-of-the-art methods by capturing contextual dependencies between biomedical entities to infer potential DDIs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!