Search

ObjectiveDiagnosis is a cornerstone of clinical practice for mental health care providers, yet traditional diagnostic systems have well-known shortcomings, including inadequate reliability, high comorbidity, and marked within-diagnosis heterogeneity. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a data-driven, hierarchically based alternative to traditional classifications that conceptualizes psychopathology as a set of dimensions organized into increasingly broad, transdiagnostic spectra. Prior work has shown that using a dimensional approach improves reliability and validity, but translating a model like HiTOP into a workable system that is useful for health care providers remains a major challenge.MethodThe present work outlines the HiTOP model and describes the core principles to guide its integration into clinical practice.ResultsPotential advantages and limitations of the HiTOP model for clinical utility are reviewed, including with respect to case conceptualization and treatment planning. A HiTOP approach to practice is illustrated and contrasted with an approach based on traditional nosology. Common barriers to using HiTOP in real-world health care settings and solutions to these barriers are discussed.ConclusionsHiTOP represents a viable alternative to classifying mental illness that can be integrated into practice today, although research is needed to further establish its utility. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background/Objective: The Alternative Model for Personality Disorders (AMPD) within the DSM‐5 includes separable components representing general personality dysfunction (Criterion A) and maladaptive personality traits (Criterion B). Some critique Criterion A for accounting for little incremental variance in PD beyond Criterion B. However, Morey et al. (2020) hypothesized that personality dysfunction is a key mechanism through which normal‐range traits account for the maladaptive component of personality traits, justifying its inclusion. We sought to replicate and extend this work in a psychiatric sample with mixed methods. Method: In total, 152 participants recruited from mental health clinics completed multiple measures of personality dysfunction and normal‐range and maladaptive traits. Results: Replication was only partially achieved. The degree of incremental prediction of maladaptive traits and the extent to which personality dysfunction explained the relations between normal‐range and maladaptive traits varied significantly across traits, and those effects that reached significance were small in magnitude. Removing variance due to personality dysfunction reduced intercorrelations among maladaptive traits by only a small amount. Conclusion: Counter to Morey et al. (2020), our results failed to support maladaptive traits as composites of normal‐range traits and personality dysfunction, suggesting that other methods of distinguishing personality pathology severity and style are needed. [ABSTRACT FROM AUTHOR]

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.

Personality pathology is increasingly conceptualized within hierarchical, dimensional trait models. The Comprehensive Assessment of Traits Relevant to Personality Disorders (CAT-PD) is a pathological-trait measure with potential to improve on currently prevailing instruments because it has wider content coverage; however, its domain-level structure, which is of scientific and clinical interest, is not established. In this study, we investigated the structure and construct validity of the CAT-PD's domain level to facilitate wider use of the measure. We estimated five- and six-factor models with exploratory factor analysis in a pooled sample of eight independent subsamples (N = 3,987) and found that both models fit the data well; each had interpretable factors that were invariant across gender, sample type, and Black/White racial groups; and the factors had good convergent validity with other measures of maladaptive traits, Big Five personality, and interpersonal problems. Our results support the validity of the CAT-PD for assessing multiple levels of the pathological trait hierarchy. [ABSTRACT FROM AUTHOR]

The Schedule for Nonadaptive and Adaptive Personality-Youth Version (SNAP-Y) is a new, reliable self-report questionnaire that assesses 15 personality traits relevant to both normal-range personality and the alternative "DSM"-5 model for personality disorder. Community adolescents, 12 to 18 years old (N = 364), completed the SNAP-Y; 347 also completed the Big Five Inventory-Adolescent, 144 provided 2-week retest data, and 128 others completed the Minnesota Multiphasic Personality Inventory-Adolescent. Outpatient adolescents (N = 103) completed the SNAP-Y, and 97 also completed the Minnesota Multiphasic Personality Inventory-Adolescent. The SNAP-Y demonstrated strong psychometric properties, and structural, convergent, discriminant, and external validities. Consistent with the continuity of personality, results paralleled those in adult and college samples using the adult Schedule for Nonadaptive and Adaptive Personality-Second Edition (SNAP-2), from which the SNAP-Y derives and which has established validity in personality-trait assessment across the normal-abnormal continuum. The SNAP-Y thus provides a new, clinically useful instrument to assess personality traits and personality pathology in adolescents.

Two primary limitations of research on the association between adverse childhood experiences (ACEs) and personality disorder (PD) are (1) failure to consider mechanisms of association and (2) inconsistent results due, in part, to inconsistent approaches to quantifying ACE exposure. The current study will address these limitations by examining the cross‐sectional mediating role of self‐ and interpersonal dysfunction on the association between ACE and three PDs (antisocial, schizotypal, and borderline) using three quantifications of ACE exposure (cumulative, individual, and unique risk). Participants were 149 current or recent psychiatric patients, and data analyses were performed through estimation of a series of cross‐sectional mediation models. Taken together, results suggest that (1) the association between ACE and PD is moderate, (2) self‐ and interpersonal dysfunction cross‐sectionally mediate this association, (3) after accounting for variance shared among ACEs, associations between specific ACE subtypes and PD were negligible, (4) much of the association between ACE and PD is accounted for by general processes impacted by all forms of ACE and implicated in all forms of PD, and (5) emotional neglect may uniquely contribute to self‐ and interpersonal dysfunction and thereby, PD risk. [ABSTRACT FROM AUTHOR]

Recently, integrative, hierarchical models of personality and personality disorder (PD)--such as the Big Three, Big Four, and Big Five trait models--have gained support as a unifying dimensional framework for describing PD. However, no measures to date can simultaneously represent each of these potentially interesting levels of the personality hierarchy. To unify these measurement models psychometrically, we sought to develop Big Five trait scales within the Schedule for Nonadaptive and Adaptive Personality-Second Edition (SNAP-2). Through structural and content analyses, we examined relations between the SNAP-2, the Big Five Inventory (BFI), and the NEO Five-Factor Inventory (NEO-FFI) ratings in a large data set (N = 8,690), including clinical, military, college, and community participants. Results yielded scales consistent with the Big Four model of personality (i.e., Neuroticism, Conscientiousness, Introversion, and Antagonism) and not the Big Five, as there were insufficient items related to Openness. Resulting scale scores demonstrated strong internal consistency and temporal stability. Structural validity and external validity were supported by strong convergent and discriminant validity patterns between Big Four scale scores and other personality trait scores and expectable patterns of self-peer agreement. Descriptive statistics and community-based norms are provided. The SNAP-2 Big Four Scales enable researchers and clinicians to assess personality at multiple levels of the trait hierarchy and facilitate comparisons among competing big-trait models. (Contains 7 tables and 2 footnotes.)

The State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA; M. J. Ree, C. MacLeod, D. French, & V. Locke, 2000) was designed to assess cognitive and somatic symptoms of anxiety as they pertain to one's mood in the moment (state) and in general (trait). This study extended the previous psychometric findings to a clinical sample and validated the STICSA against a well-published measure of anxiety, the State-Trait Anxiety Inventory (STAI; C. D. Spielberger, 1983). Patients (N = 567) at an anxiety disorders clinic were administered a battery of questionnaires. The results of confirmatory factor analyses (Bentler-Bonnett nonnormed fit index, comparative fit index, and Bollen fit index greater than 0.90; root-mean-square error of approximation less than 0.05); convergent and discriminant validity analyses; and group comparisons supported the reliability and validity of the STICSA as a measure of state and trait cognitive and somatic anxiety. In addition, compared with the STAI (anxiety: rs = 0.52; depression: rs = 0.64), the STICSA was more strongly correlated with another measure of anxiety (rs = 0.67) and was less strongly correlated with a measure of depression (rs = 0.61). These findings suggest that the STICSA may be a purer measure of anxiety symptomatology than is the STAI.

The authors describe a new self-report instrument, the Inventory of Depression and Anxiety Symptoms (IDAS), which was designed to assess specific symptom dimensions of major depression and related anxiety disorders. They created the IDAS by conducting principal factor analyses in 3 large samples (college students, psychiatric patients, community adults); the authors also examined the robustness of its psychometric properties in 5 additional samples (high school students, college students, young adults, postpartum women, psychiatric patients) who were not involved in the scale development process. The IDAS contains 10 specific symptom scales: Suicidality, Lassitude, Insomnia, Appetite Loss, Appetite Gain, Ill Temper, Well-Being, Panic, Social Anxiety, and Traumatic Intrusions. It also includes 2 broader scales: General Depression (which contains items overlapping with several other IDAS scales) and Dysphoria (which does not). The scales (a) are internally consistent, (b) capture the target dimensions well, and (c) define a single underlying factor. They show strong short-term stability and display excellent convergent validity and good discriminant validity in relation to other self-report and interview-based measures of depression and anxiety.

Conceptual overlap and heterogeneity have long been noted as weaknesses of the Minnesota Multiphasic Personality Inventory's clinical scales. Restructured clinical (RC) scales recently were developed to address these concerns (A. Tellegen et al., 2003). The authors evaluated the psychometric properties of the RC scales in psychology clinic clients (N=285) and military veterans (N=567). The RC scales were as internally consistent as the clinical scales and correlated strongly with their original counterparts (except for RC3/Hysteria). They also were less intercorrelated, produced conceptually clearer relations with measures of personality and psychopathology, and yielded somewhat greater incremental utility than the clinical scales. Thus, the RC scales demonstrated several psychometric strengths while utilizing 60% fewer items, but the 2 sets of scales cannot be used interchangeably. Interpretive considerations are discussed.

This is a validation study of a computerized adaptive (CAT) version of the Schedule for Nonadaptive and Adaptive Personality (SNAP) conducted with 413 undergraduates who completed the SNAP twice, 1 week apart. Participants were assigned randomly to 1 of 4 retest groups: (a) paper-and-pencil (P&P) SNAP, (b) CAT, (c) P&P/CAT, and (d) CAT/P&P. With number of items held constant, computerized administration had little effect on descriptive statistics, rank ordering of scores, reliability, and concurrent validity, but was preferred over P&P administration by most participants. CAT administration yielded somewhat lower precision and validity than P&P administration, but required 36% to 37% fewer items and 58% to 60% less time to complete. These results confirm not only key findings from previous CAT simulation studies of personality measures but extend them for the 1st time to a live assessment setting.

Facial emotion recognition (FER) tasks are often digitally altered to vary expression intensity; however, such tasks have unknown psychometric properties. In these studies, an FER task was developed and validated—the Graded Emotional Face Task (GEFT)—which provided an opportunity to examine the psychometric properties of such tasks. Facial expressions were altered to produce five intensity levels for six emotions (e.g., 40% anger). In Study 1, 224 undergraduates viewed subsets of these faces and labeled the expressions. An item selection algorithm was used to maximize internal consistency and balance gender and ethnicity. In Study 2, 219 undergraduates completed the final GEFT and a multimethod battery of validity measures. Finally, in Study 3, 407 undergraduates oversampled for borderline personality disorder (BPD) completed the GEFT and a self-report BPD measure. Broad FER scales (e.g., overall anger) demonstrated evidence of reliability and validity; however, more specific subscales (e.g., 40% anger) had more variable psychometric properties. Notably, ceiling/floor effects appeared to decrease both internal consistency and limit external validity correlations. The findings are discussed from the perspective of measurement issues in the social cognition literature. [ABSTRACT FROM AUTHOR]

Converging lines of evidence have called into question the validity of conceptualizations of posttraumatic stress disorder (PTSD) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM: American Psychiatric Association, 2000) and suggested alternative structural models of PTSD symptomatology. We conducted a meta-analysis of 40 PTSD studies (N = 14,827 participants across studies) that used a DSM-based measure to assess PTSD severity. We aggregated correlation matrices across studies, and then applied confirmatory factor analysis to the aggregated matrices to test the fit of competing models of PTSD symptomatology that have gained support in the literature. Results indicated that both prominent 4-factor models of PTSD symptomatology yielded good model fit across subsamples of studies: however, the model comprising Intrusions, Avoidance, Hyperarousal. and Dysphoria factors appeared to fit better across studies. Results also indicated that the best fitting models were not moderated by measure or sample type. Results are discussed in the context of structural models of PTSD and implications for the diagnostic nosology. Keywords: posttraumatic stress disorder, confirmatory factor analysis, meta-analysis, correlation matrices, structural equation modeling DOI: 10.1037/a0020981