JOURNAL ARTICLE
A Bridge Between DSM-5 Section II Personality Disorder Criteria and ICD-11 Personality Disorder Trait Domains.
Published In: Journal of Personality Disorders, 2023, v. 37, n. 3. P. 317 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Levin-Aspenson, Holly F.; Khoo, Shereen; Stanton, Kasey; King, Brittany; Zimmerman, Mark 3 of 3
Abstract
The organization of personality pathology into trait domains (vs. specific disorders) in ICD-11 represents an important shift in personality disorder (PD) nosology. However, to facilitate clinical implementation, a bridge is needed between this system and the DSM-5 Section II system familiar to many researchers and clinicians. In this study, individual DSM-5 PD criteria were assigned to ICD-11 trait domains based on the published Clinical Descriptions and Diagnostic Requirements. This scoring scheme was examined empirically alongside DSM-5 PD dimensions (using SIDP ratings from the MIDAS project; N = 2,147 outpatients) in terms of descriptive properties and relations with psychosocial morbidity and functioning. Most PD criteria could be matched to at least one ICD-11 trait domain, indicating considerable cross-system continuity. However, points of incongruity are noteworthy for research and clinical applications. Results provide key information for bridging categorical and dimensional frameworks, indicating that the shift toward trait-based PD models need not be as disruptive as feared. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2023/06, Vol. 37, Issue 3, p317
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0885-579X
- DOI:10.1521/pedi.2023.37.3.317
- Accession Number:164584431
- 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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