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In typical Dark Triad (DT) questionnaires, generic items oftentimes refer to "others" or "people" in general. Hence, respondents have to mentally aggregate their behavior across several kinds of "others" (e.g., work colleagues, family members, and friends). It remains unknown if individuals consider different kinds of interaction partners equally or if their self-reports contain "hidden" interaction partner-specific tendencies. To shed light on this issue, we assessed generic and contextualized DT items (referring to family, friends, work, and strangers; N = 814 from the general population). The correlated trait-correlated (method − 1) model was used to investigate preregistered research questions. On average, generic DT items showed the strongest association with work-contextualized DT items and the weakest association with family-contextualized DT items. However, the associations varied considerably across DT items and traits. In sum, our results suggest that hidden framings exist in some DT items, which may impact their ability to predict relevant criteria due to contextual (a)symmetries. The generalizability of the findings to other DT instruments, items, and participant groups should be examined in future research. [ABSTRACT FROM AUTHOR]

Objective: Depressive symptom dynamics, including change trajectories and symptom variability, have been related to therapy outcomes. However, such dynamics have often been examined separately and related to outcomes of interest using two-step analyses, which are characterized by several limitations. Here, we show how to overcome these limitations using location-scale models in a dynamic structural equation modeling framework. Method: We introduce location-scale modeling in an accessible manner to pave the way for its use in research integrating within-person dynamics and intervention-related change in psychopathology, and we illustrate this modeling approach in a large-scale internet-based intervention for depression (N = 1,656). Using eight data points sampled across about 8 weeks, we predicted improvement across the intervention (50% symptom reduction) as a function of early change and symptom variability. Results: Early symptom change was associated with a more likely improvement across therapy. Variability of symptoms beyond change trajectories during the intervention was associated with less likely improvement. Conclusions: Location-scale models, and dynamic structural equation modeling more generally, are well suited to modeling how patterns of symptom change during psychotherapy are related to important (e.g., therapy) outcomes. Our illustrative application of location-scale modeling showed that symptom variability was associated with less overall improvement in depressive symptoms. However, this finding requires replication with more intensive sampling of symptoms before final conclusions can be drawn on when and how to distinguish maladaptive from adaptive variability during psychotherapy. What is the public health significance of this article?: During psychotherapy, multiple mechanisms of change should work simultaneously and in concert. As this study highlights, it is important to examine multiple types of symptom dynamics during psychotherapy simultaneously to understand the processes and ultimately the causes of therapeutic change. [ABSTRACT FROM AUTHOR]

Initial studies have presented the "Music-based Scale for Autism Diagnostics" (MUSAD) as a promising DSM-5-based observational tool to identify autism spectrum disorder (ASD) in adults with intellectual disability (ID). The current study is the first to address its clinical utility in a new sample of 124 adults with ID (60.5% diagnosed with ASD). The derived diagnostic algorithm differentiated well between individuals with and without ASD (sensitivity 79%, specificity 74%, area under the curve = 0.81). Inter-rater reliability, assessed by the scorings of four independent experts in 22 consensus cases, was excellent (ICC = 0.92). Substantial correlations with scores from other ASD-specific measures indicated convergent validity. The MUSAD yields accurate and reliable scores, supporting comprehensive ASD diagnostics in adults with ID.

Background: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Methods: Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. Results: Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. Conclusions: Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness. [ABSTRACT FROM AUTHOR]

The study assessed the diagnostic validity of the diagnostic behavioral assessment for autism spectrum disorders-revised (DiBAS-R; 19-item screening scale based on ratings by caregivers) in a clinical sample of 381 adults with ID. Analysis revealed a sensitivity of 0.82 and a specificity of 0.67 in the overall sample (70.3% agreement). Sensitivity (0.79) and specificity (0.84) were balanced in individuals with mild to moderate ID (83.3% agreement), while specificity was lower in individuals with severe to profound ID (sensitivity: 0.83, specificity: 0.34, 51% agreement). The level of ID as well as its interaction with ASD explained a significant proportion of the variance in the DiBAS-R scores. The DiBAS-R is an adequate screening tool, especially in individuals with mild to moderate ID.

Diagnosing Autism Spectrum Disorders (ASD) is important throughout the lifespan. The objective was to investigate the transcultural diagnostic validity of the Social Communication Questionnaire (SCQ) in a clinical sample of 451 adults with Intellectual Developmental Disorder (IDD) with and without ASD in Germany, the U.S.A. and Great Britain. Variables associated with higher SCQ sum-scores were higher levels of IDD, male gender, a diagnosis of ASD and the study site (Germany > U.S.A > G.B.). An ROC analysis revealed a cut-score of 13, which resulted in a sensitivity of 0.87 and a specificity of 0.58. It is recommended to adjust the cut-score according to level of IDD and gender. Further research is needed to align diagnostic assignment of ASD across different sites and countries.

Symmetrical bifactor models are frequently applied to diverse symptoms of psychopathology to identify a general P factor. This factor is assumed to mark shared liability across all psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models of P often yield anomalous results, including but not limited to nonsignificant or negative specific factor variances and nonsignificant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this article, we demonstrate why these anomalies alter the substantive meaning of P such that it (a) does not reflect general liability to psychopathology and (b) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-(S −1) approach. This method avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a well-defined general factor that can be compared across studies when researchers hypothesize what construct they consider "transdiagnostically meaningful" and measure it directly. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against specific variants of bifactor structure. [ABSTRACT FROM AUTHOR]

Background: Participants for efficacy studies on Internet-based interventions (IBI) are recruited through self-selected convenience sampling or targeted external invitations. Comparisons are necessary to determine how recruitment strategies influence sample composition. Such comparisons consequently yield potential for evaluating which conditions are more probable to fulfill IBI's promise to be an effective treatment option for underserved populations. Methods: A comparison of sociodemographic and clinical characteristics of differently recruited participants in an IBI for mild to moderate depression was made: (1) individuals on sick leave due to an affective disorder who were informed about the IBI by their health insurance (n= 308) and (2) individuals who self-selected to participate (e.g., after seeing information on the IBI in print media; n= 699). Results: Compared to the self-selected group, the externally selected group was older, less educated, spent less time on the Internet, had a less positive attitude towards IBI, and comprised more men. Groups did not differ in initial symptom severity, in-treatment symptom change, or previous psychotherapeutic experiences. Conclusions: The results indicate that larger shares of underserved subpopulations (e.g., less educated, older, male, less internet-savvy) begin an IBI treatment when the insurer informs them about the IBI. Recruitment source does not influence efficacy. [ABSTRACT FROM AUTHOR]

Background: Cognitive behavioral therapy (CBT) in a group or individual format has proven effective for treating social anxiety in numerous randomized controlled trials. Studies in routine care are rare and focus exclusively on social anxiety disorder (SAD). Findings for avoidant personality disorder (AVPD) are missing. The current effectiveness study evaluates a combination of group and individual CBT for patients with SAD with or without AVPD. Methods: One hundred and five patients diagnosed with SAD with or without comorbid AVPD completed a combination of group and individual CBT. We administered outcome measures prior to individual therapy, prior to group therapy, after group therapy, and -after individual therapy. Results: Patients experienced a strong decline in social anxiety symptoms (d = 1.1), depression (d = 1.0), and general mental distress (d = 1.1). They also showed a large increase in self-efficacy (d = 1.1). Descriptively, the largest decreases in social anxiety occurred during the group treatment. Controlling for pretreatment severity and depression, a comorbid diagnosis of AVPD was not associated with symptom improvement during treatment. Discussion: The results of the combined treatment were comparable to those of other effectiveness studies. Group treatment seemed to accelerate change in social fears. Patients with AVPD showed the same average improvements as patients without comorbid AVPD. [ABSTRACT FROM AUTHOR]

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample (N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative. [ABSTRACT FROM AUTHOR]

Hintergrund: Eine Vielzahl von randomisiert-kontrollierten Studien belegen die Wirksamkeit der kognitiven Verhaltenstherapie (KVT) für soziale Ängste. Die Wirksamkeit der KVT unter Routinebedingungen wurde allerdings seltener untersucht. In den wenigen naturalistischen Studien werden Ergebnisse ausschließlich für die soziale Angststörung (SAS) berichtet. Befunde für die ängstlich-vermeidende Persönlichkeitsstörung (ÄVPS) stehen noch gänzlich aus. Die vor-liegende Studie untersucht die Wirksamkeit einer kombinierten Gruppen- und Einzeltherapie für SAS und ÄVPS in der Routineversorgung. Methode: Einhundert und fünf Pa-tient*innen mit SAS, von welchen 36% komorbid mit ÄVPS diagnostiziert waren, nahmen an einer Kombination aus Gruppen- und Einzeltherapie teil. Die Patient*innen füllten zu Beginn der Einzeltherapie, vor der Gruppentherapie, nach der Gruppentherapie und am Ende der Einzeltherapie Fragebögen aus. Ergebnisse: Die Behandlung führte zu einer starken Verbesserung sozialängstlicher Symptome (d = 1,1), zu einer Verringerung depressiver Symptome (d = 1,0) und allgemeiner psychischer Beschwerden (d = 1,1) sowie zu einem starken Anstieg der Selbstwirksamkeit (d = 1,1). Während der Gruppentherapie nahmen die sozialen Ängste am stärksten ab. ÄVPS war kein signifikanter Prädiktor für den Therapie-erfolg. Diskussion: Die Effektstärken der kombinierten Behandlung sind mit denen anderer Studien in der Routineversorgung vergleichbar. Patient*innen mit ÄVPS erlebten ähnliche Therapieerfolge wie Patient*innen ohne ÄVPS. [ABSTRACT FROM AUTHOR]

The theory of reasoned action (ToRA) has been proposed as a framework for cyberbullying prevention design, targeting attitudes and norms. In this study effects of a long (10 weekly sessions) and a short (one day, four sessions) cyberbullying prevention program based on the ToRA were compared with a control group over 9 months. Longitudinal data from 722 students (mean age = 13.36) on cyberbullying, somatic symptoms, attitudes, and norms were analyzed within a structural equation model. Participation in the long intervention group significantly reduced cyberbullying (d = −0.584) and somatic symptoms (d = −0.316). No between‐group differences emerged for attitudes and norms. Developmental trajectories and associations were found to be as suggested by ToRA in both cross‐sectional and change‐score analyses. [ABSTRACT FROM AUTHOR]