Cannabinoids are increasingly discussed as adjuncts in addiction treatment, yet whether they improve clinically meaningful substance use disorder (SUD) outcomes beyond short-term symptom relief is unresolved. We determined whether cannabinoid exposure confers directional efficacy across opioid, alcohol, cocaine, tobacco, and methamphetamine use disorders, distinguishing symptomatic targets from sustained therapeutic outcomes.

PubMed and Embase (1975–2025) were searched for human studies evaluating cannabinoid exposure in relation to SUD outcomes. Two reviewers independently screened and extracted data. Risk of bias was assessed using RoB 2 for randomized controlled trials (RCTs), ROBINS-I for cohort studies, and JBI checklists for cross-sectional, case, and qualitative designs. Six prespecified endpoints (treatment retention, relapse, abstinence, craving, withdrawal severity, consumption) were mapped to each target SUD. Following Synthesis Without Meta-analysis (SWiM) guidance, structured narrative synthesis used a design-based weighting scheme (RCT 1.00 to qualitative 0.25). PROSPERO: CRD420251151193.

Ninety-seven studies (41,954 participants) contributed 195 endpoint instances: 89 Beneficial (45.6%), 80 No Significant Effect (41.0%), 12 Mixed/Partial (6.2%), and 14 Harmful/Inferior (7.2%). Short-term symptom targets accounted for most Beneficial findings (76.4%). Sustained outcomes were predominantly No Significant Effect, most pronounced in opioid use disorder. Beneficial symptom findings derived overwhelmingly from weaker study designs (craving 81.5%; withdrawal severity 85.7%; consumption 80.0%).

Cannabinoids confer short-horizon symptomatic benefits but do not demonstrate efficacy for sustained abstinence, relapse prevention, or retention, most clearly in opioid use disorder, where evidence is strongest. Findings for other disorders remain preliminary. Adequately powered adjunctive randomized trials with biochemically verified endpoints are needed.

Narcissistic Personality Disorder (NPD) involves disturbances in self-regulation, interpersonal functioning, and personality organization. Although traditionally characterized by grandiosity, contemporary models suggest that grandiose self-states coexist with vulnerable features such as shame, emotional dysregulation, and hypersensitivity to rejection. Recent evidence indicates that metacognitive impairments may underlie both grandiose and vulnerable narcissistic presentations; however, no study has examined how metacognition interacts with personality traits and interpersonal difficulties within an integrated system.

A cross-sectional network analysis was conducted on 287 patients with NPD. Measures included the Metacognition Assessment Interview, the Pathological Narcissism Inventory, the Personality Inventory for DSM-5 (PID-5), the Inventory of Interpersonal Problems, and SCL-90-R Depression. A Gaussian graphical model with LASSO regularization was estimated, and expected influence was used as the primary index of node centrality. Network accuracy and stability were assessed through bootstrapping procedures.

Narcissistic vulnerability was the most central node, followed by interpersonal sensitivity and metacognitive integration. Narcissistic vulnerability showed strong associations with PID-5 Negative Affectivity and Detachment, whereas narcissistic grandiosity was related to PID-5 Antagonism. Metacognitive integration occupied a central position, linking maladaptive traits and interpersonal distress. Network stability indices indicated good reliability.

Findings suggest that narcissistic vulnerability and interpersonal hypersensitivity are central aspects of dysfunction in NPD, whereas metacognitive integration appears closely associated with the organization of psychological processes within the network. Although causal inferences cannot be drawn, the results are consistent with theoretical models underlying Metacognitive Interpersonal Therapy (MIT), supporting the potential relevance of targeting integrative metacognitive capacities in NPD treatment.

Gaming disorder (GD), i.e., persistent problematic gaming with clinically significant impairment, is a growing concern; however, estimates of GD in representative adolescent samples are limited. This study aimed to examine GD prevalence, incidence, persistence, and risk factors for different epidemiological parameters across 6 years, covering pre- to post-COVID-19 pandemic.

Population-based longitudinal data, including 3358 German adolescents, were collected annually from 2019 to 2024 using online surveys. GD prevalence estimates were assessed by standardized International Classification of Diseases (ICD-11)-based self-report questionnaires. Between-wave persistence and incidence of GD, sociodemographic data, gaming time, and stress perception were measured. Risk factors for incidence and persistence of GD were examined using generalized-estimating equation (GEE) models.

GD prevalence doubled from 2.9% in 2019 to 6.5% in 2023 and remained above prepandemic levels in 2024 (3.9%). Males showed higher prevalence estimates compared to females across most waves, and prevalence peaked in 2023 (8%). For females, GD prevalence doubled between 2019 (1.7%) and 2020 (3.4%), peaked in 2022 (6.1%), and declined thereafter. The incidence of GD increased during the pandemic. According to GEE models, male sex, higher perceived stress, and longer gaming times were risk factors for GD incidence; older age and higher education were protective factors. The persistence of GD reached its peak in 2020 (36.2%). Significant risk factors for GD persistence were higher stress perception and longer gaming time.

Prevalence and incidence rates indicate that GD continues to be a significant concern, and pandemic-related increases affected both males and females. Prevention and intervention programs should target identified risk groups.

Esketamine has demonstrated efficacy in treatment-resistant depression (TRD), but medium-term real-world functional outcomes remain understudied. This study described 6-month depressive symptoms and functional trajectories during routine esketamine treatment, focusing on functional outcomes and remission correlates.

In this prospective multicenter study, 60 patients with TRD initiating intranasal esketamine augmentation were assessed at baseline, Month 1, Month 3, and Month 6 using the Montgomery-Åsberg Depression Rating Scale (MADRS) and Sheehan Disability Scale (SDS). Mixed-effects models, Turnbull interval-censored time-to-remission analysis, logistic regression, and ROC analyses were performed.

MADRS and SDS improved significantly at all follow-up time points (p < 0.001). At Month 6, symptomatic response and remission rates were 78.3 and 46.7%, while functional response and remission rates were 78.3 and 33.3%, respectively. Turnbull estimates showed cumulative functional remission rates of 5.0, 15.0, and 33.3% at Months 1, 3, and 6, respectively. MADRS-SDS correlations decreased over time, supporting partial symptomatic-functional dissociation. Higher baseline SDS (OR = 0.73, 95% CI: 0.59–0.89) and more previous antidepressant trials (ADTs; OR = 0.53, 95% CI: 0.35–0.82) were associated with lower odds of Month 6 functional remission; bootstrap internal validation supported coefficient stability. ROC-derived thresholds were considered sample-dependent and not clinically validated. Cumulative esketamine dosage was associated with functional response but not remission.

Depressive symptoms and functioning improved progressively over 6 months during esketamine treatment in routine clinical care. Functional remission followed a slower trajectory than symptomatic remission and had partly distinct correlates, supporting functional monitoring as a distinct TRD outcome.

The development of cognitive impairment (CI) is a frequent and debilitating consequence of COVID-19 and can persist for more than 1 year after the acute infection stage. Previous neuroimaging studies in COVID-19 survivors with CI have revealed widespread alterations in functional connectivity (FC), particularly within fronto-parietal circuits and subcortical nuclei such as the hippocampus, basal ganglia, and thalamus. This study focuses on neural correlates of CI in subjects who recovered from COVID-19 and the relationship between FC patterns and discrete cognitive domains.

Resting-state functional MRI data from 136 subjects were analyzed using a ROI-to-ROI approach across 246 brain regions derived from the Human Brainnetome Atlas. Group comparisons were performed based on the presence or absence of CI, and correlation analyses were conducted between FC and scores obtained from a comprehensive neuropsychological test battery.

Whole-brain FC showed no difference between patients with and without CI. In the whole study sample, hypoconnectivity between two basal ganglia regions and two frontal motor regions was associated with impaired performance in the cognitive domain “Reasoning and Problem-Solving,” while hyperconnectivity between the prefrontal thalamus and the postcentral gyrus was associated with impaired performance in the same cognitive domain.

Our findings indicate that FC alterations within the cortico-striatal and thalamo-cortical circuits may subtend deficits in higher-order executive functions in post-COVID-19 patients and highlight the importance of examining discrete cognitive domains in relation to brain connectivity.

Treatment-Resistant Late-Life Depression (TRLLD) represents a significant clinical and research challenge. Studies on TRLLD use heterogeneous diagnostic criteria limiting the interpretation of results and the development of clinical guidelines.

This study aimed to establish expert consensus on the core components and definition of TRLLD through a Delphi process conducted by a European Task Force of clinicians and researchers with experience in late-life depression.

We conducted an electronic Delphi study with 30 European experts to identify key definitional elements of TRLLD. In the 1st survey round (SR), participants responded to open-ended questions on definitions of TRLLD. Responses informed the development of 70 structured items for 2nd SR, involving 58 closed (yes/no) items and 12 multiple-choice questions across six domains: symptom presentation, cognitive impairment, comorbidities, pharmacotherapy, treatment adherence, and psychosocial factors. Consensus was defined as ≥70% agreement. In 3rd SR, participants reviewed comments and validated the final categorical definition, the operational staging model, and the derived decision algorithm for TRLLD.

Consensus was reached for 72.4% of the 58 closed items. Experts agreed that the categorical definition of TRLLD should correspond to major depressive disorder in individuals aged ≥65 years who show insufficient response to two adequate antidepressant treatments, in the absence of dementia and medical conditions that could account for depressive symptoms. An operational staging model and decision-making algorithm were developed from consensus items.

This study provides the first European consensus definition of TRLLD and highlights the need for age-adapted diagnostic criteria that reflect the clinical complexity of depression in older adults.

While both evidence-based medicine as well as personalized patient-centered medicine are glorified, clinical practice seems to be only partially influenced by guidelines or by patients’ views. Against this background, shared decision-making (SDM) became a hype but remains an ill-defined concept: it can be considered as an umbrella concept covering therapeutic alliance, physician and patient preferences, information giving, information retrieving, information receiving, patient involvement in goal setting, decision-making, and treatment choice.

A narrative review based on a literature search in PubMed and PsycINFO.

SDM in depression should move away from an ethical imperative where “one size fits all” toward a personalized approach, tailoring the medical encounter to the physician’s and patient’s expectations (knowledge, need for knowledge, and desire for degree of SDM) and to the patient’s clinical status. SDM cannot be conceived as a “snapshot” since it is a dynamic process where adjustments should be made depending on the growth of the doctor–patient alliance, the changes in expectations, and the changes in clinical status. The clinician should be more aware of barriers to effective implementation of SDM in him or herself, in the patient. The different definitions of SDM and the many reported SDM interventions (having different ingredients) hamper final conclusions on the effect of SDM on outcomes.

This narrative review aims to critically examine SDM in depression and proposes to conceptualize it as a dynamic, relational, and personalized process rather than a universal ethical standard.

Mental disorders are intergenerationally associated, particularly affecting adolescent offspring. However, the extent of such intergenerational associations among adult women in China remains unclear. This study aimed to examine the intergenerational associations of depression and anxiety between two adult female generations.

This cross-sectional study included 2,130 grandmother-mother dyads from the Grandmothers, Mothers, and Their Children’s Health study. Depression and anxiety of the grandmaternal (G0) and maternal (G1) generations were assessed using the 10-item version of the Center for Epidemiologic Studies Depression Scale and the 7-item Generalized Anxiety Disorder Scale, respectively, with scores of 10 or higher defined as depression and anxiety. Statistical analyses included logistic regression, negative binomial regression, and restricted cubic spline analyses.

A total of 11.4% G0 and 11.5% G1 participants reported depression, and 4.1% G0 and 3.1% G1 participants reported anxiety. Depression in G0 was associated with 4.29-fold (95% CI: 3.09–5.94) and 3.50-fold (95% CI: 1.98–6.01) higher odds of depression and anxiety in G1, respectively, while anxiety in G0 was associated with 3.95-fold (95% CI: 2.41–6.35) and 5.47-fold (95% CI: 2.64–10.60) higher odds of depression and anxiety in G1, respectively; dose–response relationships were also observed. In addition, the intergenerational association of depression was stronger among G0 participants residing in rural areas, whereas G0 depression and anxiety were more strongly associated with anxiety in G1 among those with higher household income.

Mental disorders were intergenerationally associated between mothers and their adult daughters. These findings emphasize the importance of family-based interventions for mental health.

Individual responses to stress are highly heterogeneous, resulting in diverse psychopathological outcomes. This variability poses challenges for traditional diagnostic frameworks and underscores the need for a transdiagnostic approach to guide interventions. This study aimed to identify distinct phenotypes within a stress-exposed population and to characterize their biological profiles using a multimodal machine learning framework.

A total of 809 stress-exposed adults (mean age 40.5 ± 8.74 years; 53.7% female) underwent clinical, laboratory, and structural MRI assessments. Data-driven clustering of clinical variables identified phenotypes, followed by machine learning classifiers trained on neuroimaging and laboratory data to predict phenotype membership. SHapley Additive exPlanations (SHAP) analysis was used to identify key biological features distinguishing each phenotype.

Three phenotypes were identified: a multi-risk group (n = 321) characterized by prominent depression, anxiety, and sleep disturbances; an alcohol-related risk group (n = 226) with high alcohol misuse and minimal comorbidity; and a resilient low-risk group (n = 262). Machine learning models accurately classified these phenotypes, indicating distinct biological profiles. SHAP analysis revealed phenotype-specific signatures: the multi-risk phenotype was associated with frontal-subcortical structural alterations and dysregulated cortisol, whereas the alcohol-related risk phenotype was characterized by frontal-insular structural alterations and metabolic abnormalities.

This study demonstrates the stratification of stress-exposed individuals into clinically and biologically distinct phenotypes. By integrating multimodal data with machine learning, we identified phenotype-specific neurobiological and metabolic profiles that extend beyond conventional diagnostic frameworks. These findings support a transdiagnostic, data-driven approach to improve risk stratification and inform personalized interventions in stress-exposed populations.

Forensic psychiatric services are expanding in many countries, and discharging patients from secure hospitals relies on accurate estimates of risk of adverse outcomes. Novel evidence-based tools for estimating one key risk, violent reoffending, have been developed in recent years. We aimed to externally validate one new tool, FoVOx, in forensic psychiatric patients sentenced to treatment and to develop an updated model (FoVOx2), incorporating additional clinical predictors.

Using Swedish national registers, we conducted a temporal external validation of FoVOx by examining 767 patients discharged between 2014 and 2023. For the FoVOx2 cohort, 906 patients discharged between 2008 and 2023 were followed up, and additional predictors tested. The outcome was violent reconviction within 12 or 24 months. Model performance was evaluated using Harrell’s C-index, area under the receiver operating characteristics curves (AUCs), calibration, and classification metrics at predefined thresholds.

In temporal validation, FoVOx showed moderate discrimination (AUCs 0.69 and 0.71; C-index = 0.69) and acceptable overall accuracy (Brier <0.11). Calibration was generally good, with mild overestimation at the highest predicted risks (>20%) at 12 months and slight underprediction at 24 months. The updated FoVOx2 model incorporated novel predictors, including clozapine treatment and additional diagnostic categories. It was associated with improved performance (AUCs 0.77; optimism-corrected C-index = 0.72; Brier 0.06 and 0.09) and demonstrated good calibration (intercept ≈ 0; slopes 1.03 and 1.05).

Updating risk assessment tools with additional clinical factors can lead to incremental improvement in model performance. Implementing tools should consider clinical utility and impact as next steps.

Cultural competence training has become increasingly common in psychiatric practice across Europe and worldwide, supported by evidence demonstrating improvements in quality of care, clinical effectiveness, and patient satisfaction. Since the publication of the original EPA guideline in 2015, the field has advanced considerably in its theoretical foundations, evidence base, and institutional support. This updated guideline addresses recent developments and challenges in research, policy, and clinical practice.

The guideline is based on a systematic meta-review of 15 systematic reviews and evidence-based guidelines on cultural competence training in psychiatric care and mental health. The evidence was synthesized to identify key concepts, educational components, implementation strategies, and current debates.

The review comprises four sections. First, it examines core elements of cultural competence training, emphasizing cultural humility, structural competence, and perspectives from the social and cultural sciences. Second, it provides practical recommendations for curriculum design, including the potential role of digital learning tools. Third, it addresses critiques of cultural competence, particularly concerns regarding essentializing and potentially racist interpretations. Finally, it explores strategies for integrating cultural competence training into existing psychiatric services, highlighting both individual and organizational factors that support implementation.

Cultural competence benefits not only migrant and ethnically diverse populations but all patients and healthcare professionals, including members of majority cultures. Successful implementation requires cultural sensitivity, critical self-reflection, and sustained commitment at individual, organizational, and system levels to achieve meaningful and lasting improvements in psychiatric care.