Screening for Personality Disorders Using the Level of Personality Functioning: Diagnostic Accuracy of the LPFS-BF 2.0 in Clinical and Sine Morbo Samples.

  • Published In: Journal of Personality Disorders, 2026, v. 40, n. 1. P. 33 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Birkás, Béla; Zsidó, András Norbert; Gergely, Sára; Tóth, Regina; Andróczi, Júlia; Kiss, Tibor Cece; Rubovszky, György Zsolt; Szabó, Rózsa Vivien; Demetrovics, Zsolt; Láng, András 3 of 3

Abstract

The current study aimed to determine cutoff scores of the Level of Personality Functioning Scale-Brief Form 2.0 (LPFS-BF 2.0) in clinical and nonclinical samples and thereby test its ability to discriminate between those samples. A total of 814 individuals participated, including a sine morbo (SM; Latin for "without any disease"; here specifically "without any mental disorder") subsample (n = 509), a clinical subsample without personality disorder (non-PD; n = 240), and a clinical subsample with personality disorder (PD; n = 65). Significant differences were found among the subsamples on all LPFS-BF 2.0 scores, with the PD subsample showing the most severe impairments, followed by the non-PD and SM subsamples. To determine cutoff scores, ROC curve analyses were conducted on 70% of the data (training set, randomly selected) and validated on the remaining set of the data (test set), controlling for age, sex, and level of education. The accuracy of the LPFS-BF 2.0 in discriminating between all three subsamples was demonstrated. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Personality Disorders. 2026/02, Vol. 40, Issue 1, p33
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:0885-579X
  • DOI:10.1521/pedi.2026.40.1.33
  • Accession Number:191632277
  • 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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