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Exploring Guilt Differences in Grandiose Narcissism, Vulnerable Narcissism, and Malignant Self-Regard.

  • Published In: Journal of Personality Disorders, 2023, v. 37, n. 3. P. 285 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pedone, Roberto; Huprich, Steven K.; Colle, Livia; Barbarulo, Anna Maria; Semerari, Antonio 3 of 3

Abstract

Narcissistic personality disorder is a heterogeneous and complex pathology which manifests itself very differently in individuals. The aim of the present study was to analyze differences and similarities in morality and sensitivity to feelings of guilt among grandiose narcissism (GN), vulnerable narcissism (VN), and malignant self-regard (MSR). We expected that MSR and VN would be most sensitive to deontological and altruistic guilt, and that MSR and VN would have higher levels of moral standards than GN. A nonclinical sample of 752 participants was evaluated. Results showed a significant association among MSR, VN, and GN. According to our hypothesis, GN turned out to be the one with the lowest association values to guilt measures. Our results demonstrated that MSR is strongly associated with all types of guilt, GN is associated with a substantial lack of guilt, and VN is associated with deontological guilt and self-hate, but not altruistic guilt. Results confirm the relevance of considering and understanding guilt when differentiating GN, VN, and MSR. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Personality Disorders. 2023/06, Vol. 37, Issue 3, p285
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:0885-579X
  • DOI:10.1521/pedi.2023.37.3.285
  • Accession Number:164584429
  • 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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