As the world's population increasingly relies on the use of modern technology, cyberbullying becomes an omnipresent risk for children and adolescents and demands counteraction to prevent negative (online) experiences. The classroom-based German preventive intervention "Medienhelden" (engl.: "Media Heroes") builds on previous knowledge about links between cognitive empathy, affective empathy, and cyberbullying, among others. For an evaluation study, longitudinal data were available from 722 high school students aged 11-17 years (M = 13.36, SD = 1.00, 51.8% female) before and six months after the implementation of the program. A 10-week version and a 1-day version were conducted and compared with a control group (controlled pre-long-term-follow-up study). Schools were asked to randomly assign their participating classes to the intervention conditions. Multi-group structural equation modeling (SEM) showed a significant effect of the short intervention on cognitive empathy and significant effects of the long intervention on affective empathy and cyberbullying reduction. The results suggest the long-term intervention to be more effective in reducing cyberbullying and promoting affective empathy. Without any intervention, cyberbullying increased and affective empathy decreased across the study period. Empathy change was not generally directly linked to change in cyberbullying behavior. "Media Heroes" provides effective teaching materials and empowers schools to address the important topic of cyberbullying in classroom settings without costly support from the outside.

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Although cyberbullying is characterized by worrying prevalence rates and associated with a broad range of detrimental consequences, there is a lack of scientifically based and evaluated preventive strategies. Therefore, the present study introduces a theory-based cyberbullying prevention program (Media Heroes; German original: Medienhelden) and evaluates its effectiveness. In a pretest-posttest design (9-month interval), schools were asked to randomly assign their participating classes to either control or intervention group. Longitudinal data were available from 593 middle school students (M Age = 13.3 years, 53 % girls) out of 35 classes, who provided information on cyberbullying behavior as well as socio-demographic and psychosocial variables. While the present results revealed worrying prevalence rates of cyberbullying in middle school, multilevel analyses clearly demonstrate the program's effectiveness in reducing cyberbullying behavior within intervention classes in contrast to classes of the control group. Hence, this study presents a promising program which evidentially prevents cyberbullying in schools.

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.

Background: Even though there is an increasing number of studies on the efficacy of Internet-based interventions (IBI) for depression, experimental trials on the benefits of added guidance by clinicians are scarce and inconsistent. This study compared the efficacy of semistandardized feedback provided by psychologists with fully standardized feedback in IBI. Methods: Participants with mild-to-moderate depression (n = 1,089, 66% female) from the client pool of a health insurance company participated in a cognitive-behavioral IBI targeting depression over 6 weeks. Individuals were randomized to weekly semistandardized e-mail feedback from psychologists (individual counseling; IC) or to automated, standardized feedback where a psychologist could be contacted on demand (CoD). The contents and tasks were identical across conditions. The primary outcome was depression; secondary outcomes included anxiety, rumination, and well-being. Outcomes were assessed before and after the intervention and 3, 6, and 12 months later. Changes in outcomes were evaluated using latent change score modeling. Results: Both interventions yielded large pre-post effects on depression (Beck Depression Inventory-II: d_IC = 1.53, d_CoD = 1.37; Patient Health Questionnaire-9: d_IC = 1.20, d_CoD = 1.04), as well as significant improvements of all other outcome measures. The effects remained significant after 3, 6, and 12 months. The groups differed with regard to attrition (IC: 17.3%, CoD: 25.8%, p = 0.001). Between-group effects were statistically nonsignificant across outcomes and measurement occasions. Conclusion: Adding semistandardized guidance in IBI for depression did not prove to be more effective than fully standardized feedback on primary and secondary outcomes, but it had positive effects on attrition.

IMPORTANCE Personalized treatment choices would increase the effectiveness of internet-based cognitive behavioral therapy (iCBT) for depression to the extent that patients differ in interventions that better suit them.OBJECTIVE To provide personalized estimates of short-term and long-term relative efficacy of guided and unguided iCBT for depression using patient-level information.DATA SOURCES We searched PubMed, Embase, PsycInfo, and Cochrane Library to identify randomized clinical trials (RCTs) published up to January 1, 2019.STUDY SELECTION Eligible RCTs were those comparing guided or unguided iCBT against each other or against any control intervention in individuals with depression. Available individual patient data (IPD) was collected from all eligible studies. Depression symptom severity was assessed after treatment, 6 months, and 12 months after randomization. DATA EXTRACTION AND SYNTHESISWe conducted a systematic review and IPD network meta-analysis and estimated relative treatment effect sizes across different patient characteristics through IPD network meta-regression. MAIN OUTCOMES AND MEASURESPatient Health Questionnaire-9 (PHQ-9) scores. RESULTSOf 42 eligible RCTs, 39 studies comprising 9751 participants with depression contributed IPD to the IPD network meta-analysis, of which 8107 IPD were synthesized. Overall, both guided and unguided iCBT were associated with more effectiveness as measured by PHQ-0 scores than control treatments over the short term and the long term. Guided iCBT was associated with more effectiveness than unguided iCBT (mean difference [MD] in posttreatment PHQ-9 scores, −0.8; 95% CI, −1.4 to −0.2), but we found no evidence of a difference at 6 or 12 months following randomization. Baseline depression was found to be the most important modifier of the relative association for efficacy of guided vs unguided iCBT. Differences between unguided and guided iCBT in people with baseline symptoms of subthreshold depression (PHQ-9 scores 5-9) were small, while guided iCBT was associated with overall better outcomes in patients with baseline PHQ-9 greater than 9. CONCLUSIONS AND RELEVANCEIn this network meta-analysis with IPD, guided iCBT was associated with more effectiveness than unguided iCBT for individuals with depression, benefits were more substantial in individuals with moderate to severe depression. Unguided iCBT was associated with similar effectiveness among individuals with symptoms of mild/subthreshold depression. Personalized treatment selection is entirely possible and necessary to ensure the best allocation of treatment resources for depression.

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 psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models often yield anomalous results, including but not limited to non-significant or negative specific factor variances and non-significant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this paper, we demonstrate why these anomalies alter the substantive meaning of P such that it (1) does not reflect general liability to psychopathology and (2) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-(S − 1) approach. This approach avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a general factor with well-defined meaning across studies. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against a specific variant of bifactor structures. In summary, bifactor-(S − 1) models provide an approach to answer questions posed in symmetrical bifactor models in a more comparable and replicable manner.

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.