While early intervention services (EIS) have demonstrated short-term benefits, the long-term maintenance of these gains remains uncertain. Individuals with first-episode psychosis exhibit significant variability in their course of recovery. Understanding the risk and protective factors that shape long-term outcome trajectories is essential to predicting and promoting sustained recovery. Here, we present the protocol for an extended 10-year follow-up study of social, mental, cognitive, and physical health outcomes, supplemented through linkage with health administrative databases to offer a holistic perspective on long-term outcome trajectories.

The primary objective of the study is to model the heterogeneity of long-term trajectories across multiple outcome dimensions over a 10-year follow-up period using data-driven methods.

The Prevention and Early Intervention Program for Psychoses (PEPP-Montreal) is a well-established, high-fidelity EIS program operating within a universal healthcare system and an epidemiologically defined catchment area in South-West Montréal, Canada. Between 2003 and 2018, PEPP-Montreal conducted a detailed two-year longitudinal assessment of 689 individuals aged 14-35 with first-episode affective or non-affective psychosis.

This study will help distinguish clinically meaningful subgroups, characterize their profiles, and identify early predictors of long-term outcomes while providing insight into the mechanisms of change within trajectories. In particular, the study will assess whether trajectories shaped during the critical period are sustained over the long term. To our knowledge, this represents the most comprehensive investigation of long-term trajectories following EIS in North America and is expected to lay the groundwork for optimizing EIS and developing personalized interventions.

To determine the prevalence and patterns of aggressive behaviour and identify factors associated with aggressive behaviour among patients with severe mental illness (SMI) attending a tertiary hospital in Northwest Ethiopia in 2025.

Institution-based cross-sectional study.

University of Gondar Comprehensive Specialised Hospital, Northwest Ethiopia, conducted from 1 to 30 March 2025.

A total of 423 patients with SMI were selected using a systematic random sampling technique.

Data were collected using interviewer-administered questionnaires and medical record review. Aggressive behaviour was assessed using the Modified Overt Aggression Scale (MOAS). Sociodemographic, clinical, substance use, medication adherence and psychosocial characteristics were assessed using standardised instruments and medical records. Adjusted ORs (AORs) with 95% CIs were estimated, and statistical significance was declared at p<0.05.

The prevalence of aggressive behaviour was 32.2% (95% CI 28.0% to 36.9%). Verbal aggression was the most common subtype (25.5%), whereas auto-aggression was the least common (12.4%). Poor medication adherence (AOR = 3.31; 95% CI 1.24 to 8.86), current substance use (AOR = 7.31; 95% CI 2.41 to 22.10), previous history of aggression (AOR = 2.82; 95% CI 1.03 to 7.74), moderate illness severity (AOR = 2.59; 95% CI 1.19 to 5.66) and severe illness severity (AOR = 4.71; 95% CI 1.12 to 19.78) were independently associated with aggressive behaviour.

One-third of patients with SMI exhibited aggressive behaviour. Poor medication adherence, current substance use, previous history of aggression and severe illness severity were independently associated with aggressive behaviour. Routine assessment of aggression risk and interventions targeting medication adherence, substance use and illness severity may help reduce aggressive behaviour among patients with SMI.

To explore how people living with HIV (PLHIV) perceive the factors that influence the effectiveness and long-term sustainability of HIV support groups in Nigeria.

Exploratory qualitative study using semi-structured focus group discussions (FGDs).

Twelve health facilities across Nigeria's six geopolitical zones, representing diverse urban and rural contexts affiliated with the Nigeria Implementation Science Alliance and ICON 3 Practice-Based Research Network (ICON-3 PBRN).

110 PLHIV, including support group members and leaders, participated in 12 FGDs. Participants were recruited purposively based on experience with support groups and HIV care.

FGDs were conducted between March and May 2025, audio-recorded, transcribed verbatim and analysed using reflexive thematic analysis. The psychosocial model of mental health guided inquiry and interpretation.

Support groups were perceived as transformative psychosocial and informational spaces that foster belonging, reduce isolation and enhance health literacy and treatment adherence. Key facilitators included vocational skill-building, cooperative income-generating activities and opportunities for economic self-sufficiency. Barriers included stigma and fear of disclosure, financial hardship (especially transport costs and loss of income), leadership instability, weak communication systems, inadequate infrastructure and breaches of confidentiality. Sustainability was linked to improved leadership, livelihood support, community awareness, reliable funding and multi-sectoral partnerships involving NGOs, philanthropists and health facilities.

Support groups in Nigeria function as critical psychosocial and economic support systems for PLHIV but remain constrained by structural, economic and stigma-related challenges. Strengthening their sustainability requires integrating psychosocial and livelihood interventions, improving governance structures and developing multisectoral collaborations. Findings provide context-specific direction for enhancing chronic illness support models in low-resource settings.

Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been highly successful in language and vision applications, allows us to account for the fact that the most recently observed time points in an individual's history may not always be the most important for the immediate future. This is achieved by assigning attention weights to observations of an individual based on a transformation of their values. One reason why these ideas have not yet been fully leveraged for longitudinal cohort data is that typically, large datasets are required. Therefore, we present a simplified transformer architecture that retains the core attention mechanism while reducing the number of parameters to be estimated, to be more suitable for small datasets with few time points. Guided by a statistical perspective on transformers, we use an autoregressive model as a starting point and incorporate attention as a kernel-based operation with temporal decay, where aggregation of multiple transformer heads, that is, different candidate weighting schemes, is expressed as accumulating evidence on different types of underlying characteristics of individuals. This also enables a permutation-based statistical testing procedure for identifying contextual patterns. In simulation studies, including a controlled simulation on real clinical predictor data, the approach is shown to recover contextual dependencies even with a small number of individuals and time points. In an application to data from a resilience study, we identify temporal patterns in the dynamics of stress and mental health. This indicates that properly adapted transformers can not only achieve competitive predictive performance, but also uncover complex context dependencies in small data settings.

Remote, scalable cognitive assessment could improve detection and longitudinal monitoring of age-related cognitive change, but few studies have validated comprehensive digital neuropsychological batteries administered entirely at home.

This study aims to evaluate the feasibility, acceptability, and short-term test-retest reliability of a remotely delivered digital neuropsychological battery (portable automated rapid testing [PART]) in cognitively healthy older adults.

We screened 82 English-speaking, cognitively healthy older adults aged 50 to 85 years and mailed configured tablets to participants. Researchers remotely administered a battery of neuropsychological assessments spanning language fluency (Boston Naming Test, verbal fluency: letter "F," supermarket items, and animals), memory (verbal paired associates, word list recall, and logical memory recall), praxis memory (clock drawing and constructional praxis [CP]), and executive functioning (trail making test [TMT]) using the PART app twice, approximately 1 month apart. Task comfortability was summarized with a comfort index based on participants' self-reports after completing each task. Reliability analyses included paired t tests to detect group-level shifts in performance, Pearson correlations for short-term stability, and Bland-Altman limits of agreement (LoA) to quantify bias and within-person variability. Performance across all tasks was also compared to comparative samples from large normative studies using equivalent paper-and-pencil versions of these tasks.

A total of 72 participants completed the first time point (T1), 64 completed the second time point (T2), and the analytic sample comprised a total of 63 participants. Overall comfort was high across sessions (mean comfort index was 88% at T1 and 84% at T2). Memory and executive function measures showed moderate test-retest correlations (r=0.39-0.73), while clock drawing and CP showed low or nonsignificant associations, likely due to ceiling effects. Paired tests indicated small but significant practice effects for verbal paired associates (t₆₂=-3.10; P=.003) and word list (t₅₇=-2.50; P=.02), with substantial practice effects for logical memory (story A: t₅₆=-2.95; P=.005) and TMT part A (t₅₀=-3.97; P<.001) and part B (t₄₂=-2.05; P=.05). Bland-Altman analyses revealed minimal mean bias for many tasks but notably wide LoA for TMT (part A: LoA=-44.7 to 29.2 seconds; part B: LoA=-78.1 to 57.9 seconds). Importantly, in descriptive comparisons, our sample generally overlapped within ±1 SD of the medians in comparative paper-and-pencil normative samples.

PART was well tolerated among healthy older adults and reproduces group-level normative patterns with moderate short-term reliability for several conventional measures. However, nontrivial within-person variability, practice effects, ceiling effects, and wide LoAs for some measures (eg, TMT, CP, and clock drawing) limit interpretation of individual change. Future work should validate alternative digital outcome metrics (eg, stylus interactions and speech recordings), extend sampling to more diverse and lower-education cohorts, and evaluate more ecologically valid designs to improve sensitivity for detecting meaningful within-person cognitive fluctuations.

Digital overuse poses a significant threat to health through multiple pathways, including the deterioration of mental health and sleep disturbance. This issue has led to the development of digital overuse-targeted digital behavior change interventions (DO-DBCIs). Although research in this field has advanced by adopting state-of-the-art technologies, substantial gaps exist in elements critical to establishing intervention validity, including target devices and activities, intervention strategies, theoretical foundations, target populations, and sustainability of effects.

This scoping review aimed to systematically map DO-DBCI research across these five dimensions and to identify structural gaps and future research priorities.

Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 4 databases (Web of Science, ACM Digital Library, IEEE Xplore, and PubMed) were searched on August 4, 2026, for studies published through 2025. Of 3260 records retrieved, 32 studies were included after 2-stage screening by 2 independent reviewers.

Pronounced structural biases were identified across all 5 dimensions. Most studies targeted smartphones exclusively (27/32, 84%) and aimed at reducing total usage time without distinguishing specific activities (26/32, 81%); no study addressed short-form video platforms. Self-monitoring was the most frequently identified strategy, followed by nudge and friction, while AI- and machine learning (ML)-based interventions remained limited. Of the 32 studies, 12 (38%) lacked an explicit theoretical framework. Target populations were heavily skewed toward young adults (17/31, 55%), with only 1 of 31 studies (3%) targeting adolescents. Most studies adopted short-term designs of 4 weeks or less, and only 38% (12/32) included any postintervention follow-up.

This first comprehensive scoping review of DO-DBCI identifies 4 critical gaps-device and activity concentration, theory-to-design disconnect, demographic skew toward convenience samples, and insufficient longitudinal evaluation. Future research should prioritize activity-specific interventions, explicit theory-driven design, expanded population diversity, and standardized longitudinal study designs.

Objective: Evidence for predictors of long-term outcomes in personality disorder (PD) treatment remains limited. Machine learning (ML) shows promise in early prediction of treatment outcome to inform clinical practice. This study evaluates the performance of various ML algorithms and examines variables that predict long-term outcomes.Method: Personality functioning was assessed one-year post-treatment in 524 patients who received specialized outpatient psychotherapy for PD, using baseline predictors (demographic and clinical variables at intake) and post-treatment predictors (treatment and clinical variables at treatment end). Regularized linear regression methods were compared with more complex tree-based approaches using cross-validation. Predictive performance was assessed with Mean Absolute Error (MAE) between true and predicted scores.Results: Baseline-only predictors achieved an MAE of 0.40-0.44 (10-11% on a 0-4 scale range). Post-treatment predictors substantially reduced the MAE to 0.27-0.33 (7-8%). Only in the latter case did complex models perform slightly better. Predictors were anhedonia, self-esteem, and relational functioning.Conclusion: Baseline predictors alone provided limited prognostic value, while updating the model with post-treatment predictors improved performance, lending support for a forecasting approach to long-term outcome. In this tabular clinical dataset, complex models provided marginal improvements at best, making simpler models preferable for their interpretability.

Irritability is a ubiquitous clinical phenomenon, and yet its definition and specific role within psychiatric disorders have long eluded the field. By understanding what irritability is, in whom it should be studied and how it should be modelled, new insights into the disorders within which it manifests may be gained.

Interest in psychedelic medicine is increasing, yet psychiatry trainees report limited education in this area. The authors developed a standardized curriculum and evaluated its impact on trainee knowledge and attitudes toward psychedelic medicines.

A 6-h psychedelic medicine course was delivered across four psychiatry residency programs in the Northeast United States in 2025-2026. The curriculum included six modules covering foundational concepts and evidence, as well as clinical considerations. Anonymous pre- and post-course surveys assessed knowledge, interest, and confidence in counseling. Statistical comparisons were used to evaluate pre-post changes.

Forty trainees participated across institutions; 34 completed a pre-course survey and 22 completed a post-course survey. Most trainees reported minimal prior didactic exposure to psychedelic medicine. Self-assessed knowledge as well as scores on a knowledge quiz improved significantly following the course. Self-rated understanding of both the rationale for psychedelic treatments and limitations of evidence increased. Most substantially, confidence in counseling patients about clinical research, harm reduction, and treatment risks improved across domains. Baseline interest in psychedelic medicine was high at the start of the course and did not change significantly following its completion.

A multi-site curriculum in psychedelic medicine was associated with increased knowledge and counseling confidence in trainees. The curriculum did not significantly impact interest or plans to pursue opportunities in psychedelic medicine, suggesting the course did not overinflate enthusiasm for the field. Standardized curricula in residency education may help address gaps in psychiatric training as clinicians increasingly encounter questions about these emerging treatments.

Spirituality and spiritual fitness are important protective resources for healthcare professionals because they may give meaning to clinical practice, support ethical decision-making, and strengthen psychological resilience. This study aimed to adapt the SOCOM Spiritual Fitness Scale into Turkish and evaluate its psychometric properties among healthcare professionals in Türkiye. This cross-sectional validation study was conducted with healthcare professionals in the Black Sea Region of Türkiye. Of the 500 survey forms distributed, 408 were returned, yielding a response rate of 81.6%. After excluding 18 forms that did not meet the inclusion criteria, the final analyses were conducted with data from 390 participants. The scale adaptation process included forward-backward translation and cognitive interviews in accordance with international adaptation standards. Confirmatory factor analysis supported the original three-factor structure and yielded acceptable-to-good fit indices (RMSEA = 0.066, CFI = 0.961). Internal consistency was high (Cronbach's α = 0.95), and convergent validity was supported by positive correlations with the meaning in life subdimensions. Measurement invariance analyses indicated equivalence across gender, age, and occupational groups. In conclusion, the SFS-HP appears to be a valid and reliable instrument for assessing spiritual fitness among healthcare professionals in Türkiye and for producing comparable scores across different subgroups.