JOURNAL ARTICLE
Understanding Grandiose and Vulnerable Narcissism in Adult Outpatients: A Head-to-Head Comparison Between DSM-5 Section II Personality Disorders and DSM-5 Alternative Model for Personality Disorders.
Published In: Journal of Personality Disorders, 2025, v. 39, n. 2. P. 113 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Somma, Antonella; Gialdi, Giulia; Krueger, Robert F.; Markon, Kristian E.; Fossati, Andrea 3 of 3
Abstract
To compare the effectiveness of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), Section II personality disorder (PD) model, and of the Alternative Model for Personality Disorders (AMPD) model in characterizing vulnerable (VN) and grandiose (GN) narcissism, a sample of clinical psychotherapy participants (N = 369) was administered the Schedule for Nonadaptive and Adaptive Personality-2, the Levels of Personality Functioning Scale-Self Report (LPFS-SR), the Personality Inventory for DSM-5, the Five-Factor Narcissism Inventory-Short Form (FFNI-SF), and the Pathological Narcissism Inventory (PNI). In multiple regression models, the LPFS-SR scales and the Personality Inventory for DSM-5 (PID-5) domain scales explained 34.6% and 23.7% more variance than the self-reports of the 10 Section II PD symptom counts in the FFNI-SF and PNI GN scores, respectively. Similarly, AMPD measures outperformed self-reported symptom counts of the 10 Section II PDs, accounting for 28.8% and 22.6% more variance in the FFNI-SF and PNI VN scale scores, respectively. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2025/04, Vol. 39, Issue 2, p113
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:0885-579X
- DOI:10.1521/pedi.2025.39.2.113
- Accession Number:184797980
- Copyright Statement:Copyright of Journal of Personality Disorders is the property of Guilford Publications Inc. 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.)
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