JOURNAL ARTICLE
Recognizing Borderline Personality Disorder in Men: Gender Differences in BPD Symptom Presentation.
Published In: Journal of Personality Disorders, 2024, v. 38, n. 2. P. 195 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sanchious, Saivone N.; Zimmerman, Mark; Khoo, Shereen 3 of 3
Abstract
Women are predominantly diagnosed with BPD, with studies estimating a 3:1 female-to-male diagnostic ratio in clinical settings. Previous studies present conflicting findings regarding gender-level criterion differences, with some indicating differences in contradictory criteria. These studies primarily utilize outpatient samples, highlighting gaps in the literature. Thus, the current study investigates gender-level criterion differences, functioning, and impairment within a novel, partial hospital sample. Participants included (a) a sample of 1,153 individuals from the total population of partial hospital patients regardless of BPD diagnosis and (b) 365 BPD-positive patients who were assessed via semistructured clinical interview and provided consent for data collection during the intake process. Results indicated that (a) women endorsed higher relationship instability than men and (b) there were no significant differences in level of functioning across the gender subsamples. Examining gender differences in BPD symptomatology has clinical implications in improving recognition and addressing potential biases associated with men and mental health. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2024/04, Vol. 38, Issue 2, p195
- Document Type:Article
- Subject Area:Psychology
- Publication Date:2024
- ISSN:0885-579X
- DOI:10.1521/pedi.2024.38.2.195
- Accession Number:176512140
- 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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