Explores enhanced specificity in predicting outcomes for personality disorders, suggesting improved diagnoses.
Dimensional models of personality disorder (PD) may be constrained by an excessive reliance on broad domains. Research in other areas suggests that greater specificity may enhance the coverage of maladaptive traits and the prediction of clinically relevant outcomes. In a sample of 774 outpatients, we examined this possibility using the Dimensional Assessment of Personality Pathology-Basic Questionnaire (DAPP-BQ) across four hierarchical levels: domains, facets, subfacets, and items. First, we compared their relative ability to capture each of the 93 DSM PD criteria. Next, we evaluated their capacity, relative to DSM categories, to predict 55 clinical and life outcomes. Coverage of PD criteria increased by 40% when using facets rather than domains, 60% for subfacets, and 81% for items, with particularly large gains for Clusters A and B. Predictive accuracy also improved for many relevant outcomes including education, social support, psychopathology, substance use, violent behavior, arrests, self-harm, and suicide attempts. We suggest that finer-grained assessments, by avoiding the limitations of higher-order aggregation, can provide more comprehensive and accurate diagnoses, better-tailored interventions, and sounder theory. Although the lower regions of the hierarchy remain largely uncharted in PDs, they hold promise for realizing the full potential of dimensional psychopathology systems.
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Vicente et al. (2026) studied this question.
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