The article aims to address validation of the criteria matrix on training in mental health in primary care by multiprofessional residencies in mental health in Brazil. This is an evaluability study considering the theoretical-logical model, proposition of a criteria matrix and validation through the Delphi Consensus. Twenty experts from the training area participated in the residencies. The validation process took place in three rounds, with 44 criteria divided into thress dimensions and six sub-dimensions being validated. In dimension 1 "Training for work in mental health in primary care" the subdimensions (a) theoretical proposals and (b) methodological proposals were validated; in dimension 2 "Work process in mental health in primary care", the subdimensions (c) organization of the work process in mental health and (d) transdisciplinary and intersectoral work were validated; in dimension 3 "Skills for practice in mental health in primary care" the subdimensions (e) skills and attitudes for practice in mental health in primary care and (f) tools for practice in mental health in primary care were validated. The Matrix points out ways for planning and updating pedagogical projects and evaluating training in mental health in primary care in homes.

The aim of this study is to identify the occurrence of work-related stress and associate it with sociodemographic and health aspects in quilombolas living in rural communities in the north of Minas Gerais, Brazil. This is a cross-sectional study carried out in 2019 using semi-structured interviews and applying the Job Stress Scale (JSS). The analysis was carried out using descriptive statistics, univariate and multivariate analysis, using multinomial logistic regression. A positive association was identified between high-strain work and being under 40 years of age (OR: 2.32; 95%CI 1.12-4.80), working as a rural or agricultural employee (OR: 2.36; 95%CI 1.07-5.18), and low social support (OR: 2.5; 95%CI 1.30-5.09). A significant association was also observed among individuals who tested positive for common mental disorders and active work. (OR: 2.96; 95%CI 1.34-6.97). The relationship between stress and the occurrence of common mental disorders in high-burden activities and active work highlights the need for the integration of the National Workers' Health Policy, the Psychosocial Care Network, and professional training institutions, identifying and addressing needs and health issues.

Objectives. To examine individual-, school-, and neighborhood-level factors associated with COVID-19 primary and booster vaccination among New York City public school students. Methods. We analyzed data from a population-based cohort study of 917 062 students aged 5 to 18 years enrolled in 2021/2022. Enrollment data were linked to the Citywide Immunization Registry, Medicaid claims, and neighborhood indicators (January 2020-July 2023). We assessed multivariable regression associations between vaccination status and demographic, clinical, school, and contextual characteristics. Results. Overall, 51.3% of all students completed the primary series and 10.8% received a booster. Uptake was highest among non-Hispanic Asian students (72.1%; 95% confidence interval [CI] = 71.9, 72.3) and lowest among non-Hispanic Black (42.2%; 95% CI = 42.0, 42.4) and non-Hispanic White students outside Manhattan (39.7%; 95% CI = 39.5, 40.0). Students with preexisting conditions were modestly more likely to have received vaccination than peers (54.9%; 95% CI = 54.8, 55.1 vs 49.8%; 95% CI = 49.7, 49.9). Uptake increased with older age and proximity to vaccination sites but was lower among Medicaid-enrolled students. Conclusions. Coverage in the largest US school district was lower than national pediatric estimates, with only modestly higher coverage among children at elevated medical risk, despite their prioritization in vaccination efforts. Public Health Implications. Children at elevated medical risk remained undervaccinated. Addressing barriers in insurance, neighborhood, and care delivery systems is critical to improving vaccine uptake. (Am J Public Health. Published online ahead of print August 6, 2026:e1-e11. https://doi.org/10.2105/AJPH.2026.308604).

To the best of our knowledge, no previous study has assessed the relationship between sleep disturbances and quality of life in the general population of Qassim, Saudi Arabia. Therefore, this cross-sectional study aimed to evaluate the relationship between sleep disorders and quality of life and to determine how common sleep disturbances are in Qassim, Saudi Arabia. To this end, participants were randomly selected from public areas. Study data were collected through a survey covering sociodemographic details, health information, the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality, and the Short Form-12 to measure health-related quality of life. The reliability of the scales was confirmed using Cronbach's alpha. The association between sleep quality and quality of life was analyzed using t-tests, Pearson's correlation, and hierarchical multiple regression. Of 1,241 participants, 64.5% were females, 94.8% were Saudi nationals, and 64.8% were students. The mean global PSQI score was 7.38 ± 3.19, with 70.2% of participants reporting poor sleep quality. The mean physical and mental component scores were 42.6 ± 7.98 and 38.9 ± 8.03, respectively. Hierarchical multiple regression analyses indicated that after controlling for demographic characteristics, chronic disease, and body mass index, sleep-related variables significantly predicted both physical and mental health-related quality of life. The analysis showed that sleep-related factors had a substantially larger predictive contribution toward mental health (ΔR2 = 5.7%) than toward physical health (ΔR2 = 1.0%). In conclusion, this study revealed a high rate of poor sleep quality (70.2%) in Qassim, Saudi Arabia, with notable negative effects on mental health. Increasing awareness and integrating sleep assessments into public health initiatives are crucial for improving sleep quality.

Veterans with serious mental illness (SMI) smoke at disproportionately high rates but receive smoking cessation treatment at disproportionately low rates. Mental health providers have strong relationships with veterans with SMI but often do not treat smoking. Knowledge, attitudes, and self-efficacy are related to smoking treatment delivery, but their interactions have yet to be explored in qualitative data for providers working with veterans with SMI. Guided by the theory of planned behavior, this project explored how knowledge, attitudes, self-efficacy, and emerging themes impeded or facilitated smoking cessation treatment delivery in SMI clinics at a Veterans Affairs medical center. Semistructured interviews were conducted with 20 multidisciplinary providers. Data were analyzed qualitatively using an inductive-deductive approach, and the number of providers endorsing themes was quantified. Many providers had knowledge of high smoking rates among veterans with SMI and smoking harms. Though most (60%) providers asked about smoking, only 20% followed up about smoking. Medical record prompts to ask about smoking facilitated smoking treatment delivery. Providers held various attitudes toward smoking, such as viewing smoking as enjoyable for veterans and smoking discussions as a potential threat to rapport, that were related to feelings of guilt and frustration. Low self-efficacy due to limited training in smoking cessation exacerbated discomfort. Half of providers expressed motivation to increase smoking discussions. Knowledge, attitudes, emotions, and self-efficacy may interact with one another as providers decide how to treat smoking. Intensive implementation strategies designed to improve provider delivery of tobacco treatment should make sure to address all these barriers. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

This study examines perceptions of access to military sexual trauma-focused group therapy among veteran men with histories of military sexual trauma at a mental health clinic within the Veteran Affairs Greater Los Angeles Health Care System, a large and heterogeneous health care facility. Drawing on veteran data (n = 134) collected from January 2019 to April 2025, we used thematic qualitative analysis to examine veterans' perceptions of an MST-focused group therapy to which they had been referred. This study found that veteran men expressed varying perceptions of MST-focused group therapy that functioned as either barriers or facilitators to engagement. Perceived barriers included: concerns about the group format (privacy, trust, safety, efficacy), interpersonal challenges (peer judgment, fear of vulnerability, social interaction), and intrapersonal concerns (avoidance, shame, fear of retraumatization). Facilitators included: trust in referring veteran affairs providers, motivation for trauma recovery and personal growth, and interest in social connection, including reducing isolation and learning from peers. These findings offer clinical, programmatic, and systems-level implications for enhancing referral conversations and improving access to MST-focused group therapy for veteran men. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Dementia caregivers experience high levels of emotional burden, and adult day services (ADS) may provide respite that allows for recovery from caregiving demands. Yet little is known about whether ADS respite contributes to greater leisure time satisfaction-an important aspect of caregivers' respite experiences that may impact psychosocial well-being.

This secondary analysis used pooled longitudinal data from five national U.S. studies (REACH I, REACH II, Community REACH, Video to Video Network, and Caring for the Caregiving Network), totaling 2,417 dementia caregivers. Descriptive statistics and mixed-effects logistic and linear regression models with multiple imputation examined factors associated with ADS respite use and its longitudinal association with leisure time satisfaction, accounting for relevant covariates. Associations between leisure time satisfaction and psychosocial outcomes were examined.

ADS use was associated with more years of education and greater leisure time satisfaction among caregivers. Use of ADS respite was also associated with more functional limitations (IADLs) in the care recipient and less bother about care recipients' IADL impairment among caregivers. Compared with non-use, ADS use was associated with higher leisure time satisfaction. Greater leisure time satisfaction was associated with less burden and loneliness as well as more social support and positive aspects of caregiving.

ADS use was associated with greater leisure time satisfaction, which in turn was related to caregiver well-being. Interventions that promote caregivers' use of respite for satisfying activities and policies that expand access to ADS may maximize psychosocial benefits for dementia caregivers.

Clinical course information, including disease onset, episode recurrence, and hospitalization history, is essential for psychiatric care and research. However, these data are embedded in unstructured clinical narratives with substantial linguistic variability, making manual extraction labor-intensive and rule-based extraction difficult to scale. Fine-tuned large language models (LLMs) may flexibly extract such information from privacy-sensitive psychiatric records.

This study aimed to evaluate the performance of LLMs for automatically extracting temporal and clinical course information from psychiatric discharge summaries.

We analyzed 500 psychiatric discharge summaries from the Integrated Medical Database of National Taiwan University Hospital. A psychiatrist and a natural language processing researcher manually annotated clinical events and temporal information. Four open-source LLMs (LLaMA, MentaLLaMA, OpenBioLLM, and Mistral) were fine-tuned using low-rank adaptation and evaluated using 10-fold cross-validation. A 2-stage framework was developed in which sentence-level extraction of clinical events and temporal information was followed by chart-level prediction of 4 clinical course features: first-episode onset time, episode count, number of psychiatric hospitalizations, and most recent hospitalization. Performance was assessed using precision, recall, F1-score, accuracy, mean absolute error (MAE), and bootstrap significance testing.

A total of 12,947 sentences were extracted from 500 discharge summaries, yielding 7177 clinical event annotations and 4842 temporal annotations. At the sentence level, Mistral achieved the highest F1-scores for clinical event extraction, including symptom/episode detection (0.925, 95% CI 0.920-0.930), hospitalization detection (0.944, 95% CI 0.934-0.952), and remission/response detection (0.867, 95% CI 0.851-0.883). Mistral also achieved the highest F1-scores for most temporal information categories, including age expressions (0.983, 95% CI 0.973-0.993), relative time expressions (0.953, 95% CI 0.944-0.963), duration expressions (0.976, 95% CI 0.962-0.987), and vague temporal expressions (0.888, 95% CI 0.871-0.905). At the chart level, the proposed 2-stage framework showed the clearest benefit for first-episode onset prediction. Using the 2-stage framework, Mistral achieved the highest point estimate for onset accuracy (0.772), although its performance did not significantly differ from that of LLaMA using the same framework (0.744; bootstrap P=.26). For onset prediction, the 2-stage framework significantly outperformed the direct and joint extraction approaches across all evaluated models (all bootstrap P≤.002). For other chart-level features, the best-performing approach varied: using the 2-stage framework, Mistral achieved the highest episode count accuracy (0.624; MAE=0.428); using the direct approach, LLaMA achieved the highest hospitalization count accuracy (0.692; MAE=0.379); and using the 2-stage framework, OpenBioLLM achieved the highest most recent hospitalization accuracy (0.868; F1-score=0.617).

Fine-tuned, locally deployable, open-source LLMs can extract temporal and longitudinal disease course information from psychiatric discharge summaries. The 2-stage framework was most beneficial for first-episode onset prediction and performed competitively across other chart-level features, supporting the use of LLMs to transform heterogeneous psychiatric narratives into structured data for research and decision support.

While technology can widen access to mental health treatments, digital mental health interventions (DMHIs) frequently have low engagement and high dropout rates. A better understanding of user engagement with DMHIs can help researchers design technologies that users are more likely to benefit from. However, a major challenge is that the term "engagement" is very broad, not well-understood, and operationalized differently across projects. Few studies have explored how clinical researchers define and operationalize engagement in DMHI research.

This study investigated how clinical researchers operationalize user engagement with DMHIs in academic settings. Investigating operationalization can help identify gaps and inform strategies to better operationalize engagement in DMHIs according to intervention goals.

We conducted exploratory qualitative semistructured interviews via Zoom (Zoom Communications, Inc; May 2023 to February 2024) with 12 clinical mental health researchers who had developed DMHIs using human-centered design methods. We recruited participants via purposive and snowball sampling. The interviews focused on understanding what participants considered engagement, how they measured it, the strategies they used to support or increase engagement, and the barriers they faced. We inductively coded the transcripts and conducted thematic analysis, iterating on codes and themes collaboratively.

We identified 3 dimensions of engagement for DMHIs: digital mental health components (ie, intervention, technology, and human support), levels of engagement (micro and macro), and visibility of the engagement (visible and invisible). We also described the challenges of designing DMHIs for engagement. Participants described components as overlapping; some viewed the technology and intervention as one, while others viewed the technology as distinct. Users should experience components as integrated. Within the levels of engagement, clinical researchers focused on designing for macroengagement but primarily measured microengagement through quantitative measures. Participants distinguished the visibility of engagement between what was capturable and measurable (visible engagement) and what could not be captured (invisible engagement). A major barrier to engagement was overcoming the invisibility of opportune moments for users to engage with the DMHI.

Clinical mental health research focuses on end point clinical outcomes, while user engagement refers to the dynamic interaction with DMHIs in real-world settings, encompassing behavioral, cognitive, and affective user involvement. This tension highlights the need to operationalize engagement to better understand how users engage with DMHIs. A mixed method approach to capturing engagement would better align with clinical researchers' goals of understanding macroengagement. The dimensions of engagement (components, levels, and visibility) extend previous conceptualizations by providing support at the start of designing for the engagement of DMHIs. While capturing invisible aspects can still be challenging, awareness of the visibility of engagement can help researchers align their measurement and evaluation approaches with their macroengagement goals, instead of focusing on aspects of engagement that are readily visible.

Chronic stress is a driver of increased risk for disease and mental health challenges, which disproportionately affect Latine individuals assigned male at birth (AMAB). Latines are one of the fastest growing sub-populations in USA warranting urgent action to remediate inequities. Because of this, a better understanding of stress and coping pathways is needed to improve targeted programmes and interventions. Semi-structured interviews were conducted with 30 queer AMAB Latine participants aged 18 to 68 in the continental USA, representing diverse socioeconomic and life experiences. Thematic analyses explored perspectives on health, gender, masculinity, stressors, and coping strategies. Participants defined health holistically and described chronic stress as shaped by structural factors, including the sociopolitical climate, institutional barriers, family obligations, and rigid gender expectations. Participants also described actively renegotiating masculinity and drawing on cultural pride, community, and collective care as sources of resilience, wellbeing, and joy. Findings underscore the need for approaches that address systemic inequities while also attending to the existing assets queer AMAB Latine individuals draw upon in order to survive and thrive. Study findings invite a shift from deficit-based models towards frameworks that highlight cultural strengths, relational care, joy, and locally grounded practices of coping and wellbeing.