JOURNAL ARTICLE
When the Others Are Dangerous: Paranoid Presentations in Subclinical Forms of Personality Disorders.
Published In: Journal of Personality Disorders, 2024, v. 38, n. 6. P. 573 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fanti, Erika; Di Sarno, Marco; Di Pierro, Rossella 3 of 3
Abstract
The discriminant validity of paranoid personality disorder has been recently questioned, and paranoid presentations are now conceived of as transdiagnostic features of personality disorders (PDs). However, empirical results are inconsistent. This study investigated the link between subclinical personality disorders (except paranoid PD) and paranoid presentations, exploring how the severity of personality functioning affects this relationship. Nonclinical participants (N = 270, females: n = 194; 71.9%) completed self-report measures of the constructs of interest. In multiple regression analyses, subclinical borderline PD was primarily related to a wide range of paranoid presentations. Moreover, the severity of personality functioning increased the strength of the association between subclinical BPD and severe paranoid presentations. Results suggest that, when exploring unique contributions, paranoid presentations are especially associated with subclinical forms of BPD and highlight the importance of considering personality dysfunction severity. Additionally, the findings demonstrate that paranoid presentations are a relatively transdiagnostic dimension. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2024/12, Vol. 38, Issue 6, p573
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:0885-579X
- DOI:10.1521/pedi.2024.38.6.573
- Accession Number:181811046
- 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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