JOURNAL ARTICLE
Deconstructing Detachment: Contrasting Trait Profiles in Community Adults With Schizoid Versus Avoidant Personality Styles.
Published In: Journal of Personality Disorders, 2024, v. 38, n. 6. P. 520 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Urer, Feyza; Bornstein, Robert F. 3 of 3
Abstract
The degree to which schizoid and avoidant personality styles represent unique variants of interpersonal detachment remains controversial. This study contrasted core traits associated with schizoid versus avoidant personalities in a mixed-sex sample of 221 community adults, using the five traits that comprise the Alternative Model for Personality Disorders (AMPD). The International Personality Disorders Examination Screening Questionnaire was used to assess schizoid and avoidant personality traits; the Personality Inventory for DSM-5 was used to assess negative affectivity, detachment, antagonism, disinhibition, and psychoticism. As expected, schizoid and avoidant scores were both positively associated with AMPD detachment scores (rs were.68 and.57, respectively). Regression analyses confirmed that, in addition to detachment, high levels of negative affectivity and low levels of disinhibition were uniquely predictive of avoidant personality traits, whereas low levels of antagonism were uniquely predictive of schizoid personality traits. The present findings support the distinctiveness of these two contrasting expressions of detachment. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Personality Disorders. 2024/12, Vol. 38, Issue 6, p520
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2024
- ISSN:0885-579X
- DOI:10.1521/pedi.2024.38.6.520
- Accession Number:181811043
- 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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