Fivos Efstratios Papamalis

Addiction Medicine Conferences
Fivos Efstratios Papamalis
University of Essex, Greece
Title: Modular-based dimensional approach to the treatment of addiction

Abstract

The empirical dimensional based conceptualization of personality for determining functional impairment and establishing diagnosis has become prevalent and gained significant attention in the updated version of the Diagnostic and Statistical Manual (DSM-5). Well-established problems with categorical classification system such as low reliability, diagnostic comorbidity and within-disorder heterogeneity complicate research and treatment. It has been suggested that dimensional models beyond the capacity to empirically address major diagnostic pitfalls such comorbidity and heterogeneity disentangles the overlap between diagnostic categories and personality disorder types, reveals valuable information regarding lower-order traits and symptoms , and have considerable potential for designing and guiding treatment. The dimensional based assessment instead of assigning individuals to a mental disorder category in a binary approach (have or don’t have), quantifies a person’s symptoms or characteristics and denotes them with numerical values on one or more scales or continuums. Diagnosis then is not a binary process of deciding the presence or absence of the disorder but rather considers the degree in terms of symptoms count or the intensity, frequency and duration to which a particular characteristic is present. From the clinical perspective, delineating the role of personality functioning through characteristic adaptations within the treatment process could contribute to the identification of individual vulnerabilities so that they could be adequately addressed early on in order to prevent potential clinical deterioration and/or premature treatment drop out. Practically, this would imply that despite personality traits stability, interventions could moderate the degree of dysfunctional behavioural phenotypes by targeting the partially context-sensitive characteristic adaptations.