This article offers a perspective and a summary of Jack Danielian's (2010) Horneyan training model, highlighting the benefits of a meta-psychological approach for analysts in training and seasoned practitioners alike. To help illustrate the complexity of Karen Horney's views of character structure and character pathology, this article presents a model that reflects the dynamic tensions at play within individuals with narcissistic issues. It suggests that therapeutic listening can be tracked and that thematic material unfolds in a somewhat predictable, sequential, yet altogether systemic manner. Listening is not just art or intuition, nor is it merely interpretation of content based on a theoretical framework. It represents a way of holding the dialectic tension between conscious and unconscious, syntonic and dystonic. If we can better track these dynamic tensions, we can better anticipate and hopefully avoid clinical ruptures through the acting out of negative transference.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1057/ajp.2009.41 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Distribution of opioid analgesics by community racial/ethnic and socioeconomic profiles, 2011-2021.
Pain
January 2025
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.
Rapid declines in opioid analgesics dispensed in American communities since 2011 raise concerns about inadequate access to effective pain management among patients for whom opioid therapies are appropriate, especially for those living in racial/ethnic minority and socioeconomically deprived communities. Using 2011 to 2021 national data from the Automated Reports and Consolidated Ordering System and generalized linear models, this study examined quarterly per capita distribution of oxycodone, hydrocodone, and morphine (in oral morphine milligram equivalents [MMEs]) by communities' racial/ethnic and socioeconomic profiles. Communities (defined by 3-digit-zip codes areas) were classified as "majority White" (≥50% self-reported non-Hispanic White population) vs "majority non-White.
View Article and Find Full Text PDFPredicting transcriptional changes induced by molecules with MiTCP.
Brief Bioinform
November 2024
Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Studying the changes in cellular transcriptional profiles induced by small molecules can significantly advance our understanding of cellular state alterations and response mechanisms under chemical perturbations, which plays a crucial role in drug discovery and screening processes. Considering that experimental measurements need substantial time and cost, we developed a deep learning-based method called Molecule-induced Transcriptional Change Predictor (MiTCP) to predict changes in transcriptional profiles (CTPs) of 978 landmark genes induced by molecules. MiTCP utilizes graph neural network-based approaches to simultaneously model molecular structure representation and gene co-expression relationships, and integrates them for CTP prediction.
View Article and Find Full Text PDFNoninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Transl Vis Sci Technol
January 2025
School of Optometry and Vision Science, University of New South Wales, Sydney, Australia.
Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.
Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.
Transl Vis Sci Technol
January 2025
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
Purpose: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.
Methods: This cross-sectional, retrospective study used 1674 visual field (VF)-OCT pairs from 951 eyes for training and 429 pairs from 345 eyes for testing. Peripapillary retinal nerve fiber layer (RNFL) thickness map artifacts were corrected using a generative diffusion model.
Want 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!