Despite increased awareness and improved access to care, depression remains underrecognized and undertreated, in part due to limitations in how current assessment tools capture emotional distress. Traditional depression scales often rely on fixed diagnostic language and may overlook the varied and evolving ways in which individuals express depressive symptoms-particularly in digital environments. Social media platforms have emerged as important spaces where people articulate psychological suffering through informal, emotionally charged language. These expressions, while nonclinical in appearance, may hold meaningful diagnostic value.

This study aimed to develop and validate the Depression Scale for Online Assessment (DSO), a tool designed to capture ecologically valid expressions of depressive symptoms as articulated in digital contexts.

A cross-sectional, observational study was conducted with a community sample of 1216 adults, from which 1151 valid responses were retained for analysis. The scale's items were developed based on expert reviews and social media research. To identify the factor structure, exploratory factor analysis (EFA) was conducted on a randomly selected half of the sample (n=575), followed by confirmatory factor analysis on the remaining half (n=576) to validate the model. Internal consistency was assessed following the EFA, and convergent validity was examined by correlating each DSO factor score with established depression measures, including the Korean version of the Center for Epidemiologic Studies Depression Scale-Revised and the Patient Health Questionnaire-9.

EFA identified a 5-factor structure (ie, social disconnection, suicide risk, depressed mood, negative self-concept, and cognitive and somatic distress) that explained 66.53% of the total variance, indicating an acceptable level of explanatory power for a multidimensional psychological construct. confirmatory factor analysis indicated acceptable model fit (χ²₁₀₉=403.5, P<.001; comparative fit index=0.96; Tucker-Lewis index=0.95; standardized root-mean-squared residual=0.03; root-mean-square error of approximation=0.07). The scale showed high internal consistency (total Cronbach α=0.95), and subscales were significantly correlated with the Center for Epidemiologic Studies Depression Scale-Revised (r=0.68-0.77) and the Patient Health Questionnaire-9 (r=0.64-0.74), supporting convergent validity.

The DSO is a psychometrically sound and clinically relevant tool that captures both core and emerging expressions of depression. Its digital adaptability makes it especially useful for research and clinical practice in mobile and remote care settings.

The United States (US) faces a mental health crisis characterized by persistent unmet mental health needs, provider shortages, and pronounced mental health inequities for systematically marginalized communities, including the Latine population. Integrating community health workers (CHWs) into mental healthcare delivery via task-shifting is one promising approach to address longstanding inequities in treatment access. Yet, most studies of CHW mental health models have been conducted outside of the US, necessitating an evaluation of the evidence base for these models domestically, and in particular, their use with Latines living in the US. This systematic review examines the evidence for CHW-delivered mental health interventions for US-based Latines. In total, 27 articles (25 trials) met the eligibility criteria. The majority of interventions were delivered in Spanish to immigrant populations. The most common mental health targets included depression, stress, and parenting or the parent-child relationship. Common intervention components included psychoeducation, general coping skills, behavior management, case management, communication skills, relaxation, and problem solving. The majority of studies found that CHW-delivered interventions led to significant mental health symptom improvements. However, there was a high risk of bias across studies. Thus, there is promising preliminary evidence supporting CHW-delivered mental health interventions for US Latines, but more rigorous evaluation of these models is needed.

Working memory (WM) plays a pivotal role in cognitive processes; yet, its resources are strictly limited, making it crucial to understand how these resources are allocated. Motivation, such as reward and punishment, has been widely recognized as a key factor influencing WM. Previous research has either examined the impacts of reward and punishment on the availability of WM resources separately or focused solely on the effects of reward on resource allocation. We posit that integrating these effects into a single study is essential for a comprehensive understanding of their interplay. To this end, we conducted three experiments using a delay estimation task, combined with the Target Confusability Competition model, to systematically investigate how reward and punishment affect the availability and allocation of WM resources. In Experiment 1, participants memorized the orientations of two arrows, each associated with reward or punishment cues. In Experiments 2 and 3, participants encountered both reward and punishment cues within the same trial, testing three hypotheses about resource allocation: reward dominance, punishment dominance, and no difference. Results from Experiment 1 revealed that reward and punishment equally enhanced the availability of WM resources. In Experiment 2, which was the first to integrate rewards and punishments within the same trial, reward prioritized the allocation of WM resources over punishment. Eye-tracking data from Experiment 3 indicated that this effect was driven by greater attentional focus on reward targets, which led to more WM resource allocation. We provide novel evidence that reward outweighs punishment in WM resource allocation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In China, government-funded free treatment programmes have substantially expanded access to mental healthcare for patients with severe mental illnesses (SMIs). However, the effectiveness and patient satisfaction associated with these programmes can vary significantly. Understanding the factors underlying these variations is crucial for optimising patient adherence and rehabilitation outcomes.

To identify factors influencing SMI patients' satisfaction with a government-funded free treatment programme in District S, Nanjing, China, and propose targeted measures to enhance the programme's effectiveness and improve patient outcomes.

A cross-sectional study.

This study was conducted in 23 community healthcare centres in District S, Nanjing, China, from January to July 2022. A total of 924 SMI patients enrolled in the programme were selected using a two-stage random sampling method. Data on patients' individual characteristics, health behaviours and satisfaction with the programme were collected by a self-developed structured questionnaire. Guided by Andersen's Behavioural Model, hierarchical logistic regression analysis was employed to determine factors associated with patient satisfaction.

Among the 924 valid responses, 51.3% of patients with SMIs reported low satisfaction with the programme. Factors associated with patient satisfaction included individual predisposing, enabling, need and health behaviour variables. Specifically, patients who were employed, had schizophrenia, experienced adverse drug reactions, feared program exposure of their condition and received treatment in hospital were more likely to report lower satisfaction.

This study revealed that satisfaction among SMI patients with the programme remains low, with patient satisfaction primarily determined by individual need and health behaviour factors. Policymakers and stakeholders should implement targeted measures to address these factors and improve patient satisfaction.

The findings offer new insights and scientific bases for policymakers seeking to improve the programme and practical recommendations for stakeholders to develop effective solutions.

STROBE guidelines.

No patient or public contribution.

The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China's eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China.

The premorbid phase of treatment-resistant schizophrenia (TRS) may reveal underlying mechanisms and inform early interventions. According to the neurodevelopmental hypothesis, treatment resistance may be linked to pronounced developmental impairments. We examined school grades and attendance trajectories in children who later developed TRS.

This case-control study analyzed school grade point average and attendance among all individuals born after 1990 and started on clozapine in Chile's public health system as a proxy for TRS. Control groups included children later diagnosed with treatment-responsive schizophrenia, bipolar disorder, and unaffected classmates. Linear mixed models accounted for individual and school-level confounders.

We included 1072 children (9929 observations, 29.3% female) subsequently diagnosed with TRS, 323 (2802 observations, 25.7% female) with schizophrenia, 175 (1784 observations, 53.8% female) bipolar disorder, and 273,260 (533,335 observations, 47% female) unaffected classmates. Children who later developed TRS had worse grades across levels than their classmates (-0.26 SD [-0.2, -0.4]), but not treatment-responsive schizophrenia. All severe mental illness groups showed grade declines in later school levels, with TRS showing steeper linear decline than treatment-responsive schizophrenia (group×age of -0.03; 95%CI -0.04, -0.01) and steeper quadratic decline than bipolar disorder (group×age² of -0.005; -0.01, -0.001). Attendance declined over time in the two groups developing schizophrenia compared to their classmates. Those developing TRS experienced the sharpest drop (group×age compared to schizophrenia -0.03; -0.05, -0.01 and bipolar disorder -0.027; -0.049, -0.006).

TRS may stem from a more aggressive pathological process or pronounced late-maturation abnormality, rather than an early premorbid impairment, suggesting an intervention target.

The availability of diverse assessment methods, based on different devices and conditions, for respiratory sarcopenia is crucial for developing community health initiatives. Thus, in this study, we aimed to compare the concordance in diagnosing respiratory sarcopenia based on the maximal respiratory pressure (MRP) and peak expiratory flow rate (PEFR), which are specific indicators of respiratory-muscle strength, in a community-dwelling older adult population.

In the Otassha Study conducted in 2023, 499 community-dwelling adults aged >65 years in Itabashi Ward (184 men and 315 women) were evaluated for MRP, PEFR, walking speed, grip strength, and appendicular skeletal muscle mass (ASM). We determined two respiratory-sarcopenia models using low skeletal muscle mass and low MRP/PEFR (both <80% for predicted values). Pearson correlation coefficients were used to compare each measurement; the agreement between respiratory sarcopenia diagnosed using low MRP and PEFR was evaluated using kappa coefficients (κ).

PEFR was correlated with MRP (maximal expiratory pressure: r = 0.415; maximal inspiratory pressure: r = 0.380; P < 0.01, respectively). A moderate level of agreement was found between respiratory sarcopenia diagnosed using MRP and that diagnosed using PEFR (κ = 0.597, match rate = 87.2%), suggesting a fair consistency level between the methods.

Although PEFR had a moderate correlation with MRP, the concordance of respiratory sarcopenia diagnosed using the two methods was moderate, suggesting that the two approaches are partially interchangeable and are complementary. The findings showed that PEFR, proposed as an alternative to MRP, is valid for diagnosing respiratory sarcopenia, supporting its role as an alternative to the gold standard for evaluating respiratory-muscle strength. Geriatr Gerontol Int 2025; ••: ••-••.

The integration of artificial intelligence (AI) in nursing practice holds promise for enhancing patient care but is hindered by significant challenges.

This literature reviews synthesized research from 2015 to 2024, examining barriers nurses face in adopting AI technologies. A comprehensive search across multiple databases identified relevant articles, leading to the inclusion of 19 studies for thematic analysis.

The analysis revealed nine key challenges: a lack of training and education, resistance to change, data privacy and security concerns, integration issues with existing systems, high costs, limited access to technology, and regulatory and ethical dilemmas. These interconnected obstacles collectively impact the effective integration of AI in nursing practice.

Addressing these challenges is crucial for leveraging AI's full potential to improve patient outcomes. As organizations implement AI solutions, they must prioritize targeted training, infrastructure upgrades, and ethical guidelines to foster an adaptive and compliant nursing workforce.

Proactively navigating these barriers is essential for optimizing AI integration in nursing, ultimately enhancing the quality of care and operational efficiency.

The integration of AI in nursing practice necessitates enhanced training and education to equip nurses with the skills needed for effective use, fostering a culture of adaptability to technological advancements. Additionally, healthcare policies must prioritize investments in robust cybersecurity and clear regulatory frameworks to ensure ethical AI implementation while safeguarding patient data and enhancing care delivery.

East Coast Kitchen Party is a ceilidh-inspired program designed and implemented in 2022 to reduce the impacts of social isolation and food insecurity as a pathway to improving the mental wellness of lesbian, gay, bisexual, transgender, queer or questioning, intersex, and asexual (LGBTQIA+) newcomers in Halifax, Nova Scotia, Canada. Developed through a partnership between Nova Scotia Health Mental Health and Addictions Health Promotion and the YMCA Centre for Immigrant Programs, the program combined cultural cooking activities, nutrition education, and mental wellness workshops. The program emphasized peer-to-peer learning, leadership development, and culturally responsive mental wellness practices. Six sessions were held between June 2022 and February 2023, engaging 6-10 participants each. Each session invited participants to share a culturally significant recipe, fostering pride, storytelling, and connection. Discussions following the meals addressed themes such as transitioning to life in Canada and building community, with interpretation services ensuring accessibility. Evaluation through surveys and oral feedback informed iterative improvements. Challenges included food affordability, participant transience, and varying support needs based on immigration status and time in Canada. Despite these, the program successfully created inclusive spaces for LGBTQIA+ newcomers to connect, share, and heal. The initiative highlighted the importance of meeting participants where they are, recognizing the diversity within the newcomer experience, and using food as a bridge to build trust and community. The success of East Coast Kitchen Party has inspired interest in expanding the model through new partnerships, aiming to deepen connections between LGBTQIA+ newcomers and the broader community.

Ageism can negatively affect individuals' physical and mental well-being and lead to workers electing for early retirement. Generational differences among nurses impact the perception and effects of ageism within their workplace. The article discusses ageism and other manifestations of age-related discrimination among nurses, and explores strategies for nurse leaders to ensure the retention, support, and acknowledgment of older, experienced nurses.