Predicting Dropout Using DSM-5 Section II Personality Disorders, and DSM-5 Section III Personality Traits, in a (Day)Clinical Sample of Personality Disorders

Han Berghuis, Coen C. Bandell, Robert F. Krueger

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Despite the availability of structured treatments for personality disorders (PDs), still 1 in 4 patients drop out of treatment. Knowledge of whether maladaptive personality traits can lead to dropout in psychotherapeutic treatment programs of PDs is important for the purpose of a suitable indication for such treatments, especially in the light of the new alternative model of personality disorders (AMPD), which is used more and more in clinical practice. The current study investigated whether pathological personality traits of the alternative model of personality disorders, as operationalized with the Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (PID-5), and dimensional scores of PDs on the Personality Diagnostic Questionnaire-4±, could serve as predictors for dropout in an intensive (day)clinical setting for the treatment of mainly Cluster C and mild Cluster B PDs. The main finding of this study was that high scores on the PID-5 trait scales Perceptual Dysregulation, Unusual Belief and Experiences, Suspiciousness, and Rigid Perfectionism, and low scores on Restricted Affectivity and the Personality Diagnostic Questionnaire-4+ avoidant PD dimensional score, were significantly predictive for dropout from treatment.

Original languageEnglish (US)
JournalPersonality Disorders: Theory, Research, and Treatment
DOIs
StateAccepted/In press - 2020

Keywords

  • AMPD
  • Dropout
  • PID-5
  • Personality disorders
  • Personality traits

PubMed: MeSH publication types

  • Journal Article

Fingerprint Dive into the research topics of 'Predicting Dropout Using DSM-5 Section II Personality Disorders, and DSM-5 Section III Personality Traits, in a (Day)Clinical Sample of Personality Disorders'. Together they form a unique fingerprint.

Cite this