Behavioral and neuropsychiatric symptoms (primarily depression, anxiety, apathy, agitation, delusions, hallucinations, and sleep disturbances and disinhibition) can be common and challenging to manage among patients diagnosed with cognitive impairment and dementia. Management of these conditions requires a multimodal, individualized approach that emphasizes nonpharmacologic interventions first with careful use of pharmacotherapy. Comprehensive care should address comorbidities, polypharmacy, and caregiver support.

There is strong evidence supporting the efficacy of cognitive behavioral therapy (CBT) for the treatment of numerous clinical conditions commonly present in primary care. CBT interventions are time limited and goal directed, which allows them to be implemented within primary care settings. CBT techniques can be implemented effectively at the bedside at each stage of care throughout the lifespan to enhance mental and physical functioning. It is important for primary care clinicians to be empowered to utilize nonpharmacological techniques such as CBT while also recognizing the limits of their expertise and training in CBT.

The term sexual and gender minority (SGM) encompasses a diverse population whose unique health needs warrant thoughtful consideration by clinicians in primary care settings. In this article, we will provide a brief history and challenges faced by this population. Case vignettes, sticky points, and clinician care points will be presented. This paper seeks to foster reflective practice in working with SGM patients while recognizing the heterogeneity of personal identities and experiences.

Behavioural sleep disturbances are common in children with chronic health conditions. However, face-to-face psychosocial care for sleep disturbances is challenged by access barriers. Although digital health interventions may address these barriers, a comprehensive mapping of the literature on digital psychosocial interventions for sleep disturbances in this population has not yet been conducted. This scoping review provides an overview of the current literature to identify gaps and inform future research. Following the Johanna Briggs Institute's guidelines, nine databases were searched on January 21, 2025. A formal critical appraisal was not conducted due to the inclusion of quantitative, qualitative, and grey literature studies, including protocols, dissertations, and conference proceedings. Data was summarized using absolute and relative frequencies. Our search strategy returned 53 records discussing 37 distinct studies and 21 interventions (15 web-based and 6 telehealth). All interventions incorporated cognitive behavioural strategies and psychoeducation. Over half of the studies included children with neurodevelopmental conditions, the remaining with mental health (n = 6) or physical health (n = 7) conditions. Findings reported high rates of intervention acceptability and feasibility among children and caregivers and limited evidence for intervention effectiveness due to a lack of completed trials. Overall, this scoping review identifies the extent of digital sleep interventions for children with chronic health conditions; however, the absence of a formal critical appraisal of the included records limits further interpretability. Furthermore, several studies lacked an appropriate comparator group, objective sleep measures, or sufficient generalizability. Future studies should incorporate pragmatic interventions, reduce bias in study methodology, and increase participant diversity.

Machine learning (ML) and artificial intelligence (AI) offer opportunity and risk in mass trauma response, disasters and crisis. This narrative review synthesizes material from our "AI to the Rescue" panel at the inaugural PreAct Mass Trauma conference in June 2025, integrating relevant literature and the authors' expertise. We examine AI approaches beyond large language models (LLMs), including traditional ML and multimodal systems, while grounding the concept of "AI-made disasters" as a necessary third disaster type alongside Human-made and Natural, supported by emerging evidence of AI-caused psychiatric harm. We present the AI Safety Levels for Mental Health (ASL-MH) framework with six levels - from supportive applications, to autonomous packages, to experimental, high-risk systems - positioned as a practical heuristic for graduated risk governance given the nascent regulatory landscape and the demonstrated fragility of voluntary industry safety commitments. Using the Model for Adaptive Response to Complex Cyclical Disasters (MARCCD) framework, we organize AI applications across four phases: Anticipation, Impact, Adaptation, and Growth & Recovery, with attention to core disaster mental health sequelae and the challenge of differentiating normative distress from psychopathology. Recommendations address research/evidence, governance/regulation, training/literacy, and equity/access. Given our presentation involved live demos of AI applications, we have distilled key elements into this review which cannot be directly shown.

Pregnant and postpartum women encounter various health challenges, including physiological stress and mental health issues, which necessitate ongoing health monitoring. Smart bracelets present a promising solution; however, there is limited research on the preferences and willingness to pay (WTP) for such devices among this demographic.

This study aimed to investigate the preferences and WTP for smart bracelet attributes among pregnant and postpartum women in China and to explore how these preferences vary by sociodemographic factors, pregnancy stage, parity, and complications.

A cross-sectional discrete choice experiment (DCE) was conducted involving 464 pregnant and postpartum women recruited from a maternal and child health hospital in Inner Mongolia. Six key attributes were evaluated: cost, hospital backend monitoring, primary function, privacy protection, ease of use, and monitoring report frequency. A mixed logit model was used to estimate preference weights and WTP for each attribute, with subgroup analyses based on income, employment, gestational stage, parity, and other factors.

Among the 464 pregnant and postpartum women included in the final analysis (valid data rate: 96.67%), the mean age was 31.06 (SD 4.05) years. The majority of participants were of Han ethnicity (n=385, 82.97%), had a high level of education (n=422, 90.95%), resided in urban areas (n=446, 96.12%), and were employed (n=353, 76.08%). In the DCE, cost negatively impacted smart wristband preferences (β=-0.000257; P=.01). Participants exhibited a strong preference for wristbands with fetal heart monitoring (β=1.275; P<.001), high-level privacy protection (β=.541; P<.001), and ease of use (β=.973; P<.001). They were willing to pay ¥4967.45 (based on an exchange rate of US $1=CN ¥6.93) for fetal heart monitoring, ¥2975.17 for sleep monitoring, ¥2109.29 for high-level privacy protection, and ¥3437.09 for daily monitoring. Subgroup analyses indicated that preferences varied according to income, employment, pregnancy stage, parity, complications, and age.

The design of smart bracelets should be tailored to meet the diverse needs of pregnant and postpartum users. Key considerations include the integration of fetal heart and vital sign monitoring, the assurance of data privacy, the enhancement of usability, and the provision of cost-effective options. Understanding the specific preferences of different subgroups can guide the development of inclusive and responsive wearable health technologies for maternal care.

Transition from working life to retirement can challenge mental wellbeing due to loss of identity, meaning, daily structure, and social connections. This study aimed to gather expert perspectives to inform the design of Retirement Companion, a digital solution that supports mental health during the transition to retirement.

An online survey was conducted with 16 experts from psychology, academia, healthcare, social work, and human resources. Qualitative analysis was used to identify key themes related to mental health challenges, design requirements, and adoption factors.

Experts highlighted the core challenges such as loss of routines, self-worth, meaning, and social connections. Recommended features included the display of comprehensive information, tools for social connectedness, meaning-focused activities, personalized routines, peer communities, and tools for self-reflection.

The findings provide actionable design guidance for creating evidence-based and supportive digital interventions for mental health support in the transition to retirement.

Under section 3-1 of the Norwegian Mental Health Care Act, chief public health officers (CPHOs) have the authority to make decisions regarding compulsory medical examinations for individuals with suspected severe mental illness requiring treatment. This is an intrusive intervention, and procedures and practice are strictly regulated by law and regulations. Our aim was to survey the practices, organisation and administrative aspects of decisions on compulsory medical examinations by local authorities in Norway.

In autumn 2024, a digital survey was distributed to all 357 local authorities in Norway, with CPHOs as the intended recipients.

We received responses from 173 CPHOs, 115 of whom were aware of the number of decisions made by their local authority under section 3-1. Ninety-seven reported that the decisions were stored in the local authority's case management system, as required by law. Nearly all provided written justification for the decisions and signed them with their own name. Eighty-one indicated who had raised concerns in the decision and communicated the decision to the patient whenever possible.

There was a lack of comprehensive data on the extent of decisions under section 3-1 in local authorities, and practices varied considerably. This lack of oversight makes quality assurance and improvement efforts challenging.

Normative models of brain morphometry quantify individual deviations from typical anatomical patterns and hold promise for enhancing clinical decision-making. However, their clinical utility depends critically on demonstrating generalizability across diverse ethnoracial populations. We previously developed sex-specific, race-neutral normative models for cortical thickness, surface area, and subcortical volumes using brain scans from a large international sample of healthy individuals, as part of the CentileBrain Project, a global initiative to provide open-access, neuroimaging reference models. The primary aim of the present study was to empirically evaluate the generalizability and accuracy of these pretrained models across multiple ethnoracial groups. To this end, we tested model performance in independent samples of healthy individuals from Africa, Asia, Europe, and the Americas, with ethnoracial classification defined either by self-identification or genetic ancestry (N = 4,862). We further compared performance against normative models developed exclusively from a single-population Chinese cohort. Across all groups, as well as in the pooled sample, the pretrained CentileBrain models demonstrated consistently high accuracy, with relative mean absolute error values below 10% for subcortical volume and surface area and below 5% for cortical thickness. Model performance was highly concordant across self-identified and ancestry-defined groups. In a separate analysis, the CentileBrain models performed comparably to a population-specific model when applied to an independent ancestry-matched sample. These findings provide empirical support for the generalizability of race-neutral normative models developed on large and diverse samples and underscore their potential utility for individualized neuroimaging assessment across ethnoracially diverse populations.

Diverse refugee, immigrant, and migrant (RIM) populations in the United States face complex mental health needs that are shaped by migration experiences, cultural transitions, and structural barriers to accessing care. Although RIM populations have different legal statuses, cultural backgrounds, and lengths of U.S. residence, many encounter common stressors related to uncertain immigration policies, economic instability, and limited access to culturally responsive services. Recent federal actions-including suspension of the U.S. Refugee Admissions Program and revocation of "protected areas" policies-have intensified fears and mental health vulnerabilities among these populations. Using a multilevel, biopsychosocial approach, the authors aimed to provide clinical guidance on the biological, structural, sociocultural, and health care system factors that affect mental health care delivery for RIM populations. This article provides evidence-based clinical guidance and actionable recommendations that allow clinicians to selectively apply interventions (e.g., trauma-informed practices, low-barrier care models, medical-legal partnerships, and culturally responsive approaches that respect alternative healing frameworks) on the basis of individual patient circumstances. Key recommendations pertain to working with professional interpreters, navigating diagnostic ambiguity, and interfacing with immigration enforcement.