Lived experience workers are an integral part of mental health multidisciplinary teams, contributing unique insights and support grounded in personal experience and lived expertise. Despite their growing presence, challenges with the integration of lived experience workers into these teams persist. There is a gap in knowledge on lived experience workers' perspectives on their inclusion in multidisciplinary teams, as well as clinicians' views on the lived experience role. The aim for this qualitative descriptive study was to explore the experiences and perspectives of lived experience workers and clinicians regarding the integration of lived experience roles within multidisciplinary mental health teams, with a focus on identifying barriers, facilitators and impacts of role integration. Semi-structured interviews were conducted with lived experience workers (n = 7) and mental health clinicians (n = 7). Framework Analysis of interviews resulted in four main themes: (1) Systemic barriers hinder integration; (2) Lack of lived experience workforce role clarity limits impact; (3) Discipline-based defensiveness as a barrier to integration; and (4) Clinical and lived experience workforce perspectives clash. This study highlights that successful integration of the lived experience workforce in mental health care requires more than structural reform. Sustainable inclusion depends on role clarity, shared responsibility and accountability among multidisciplinary team members and relational trust. Without addressing differing attitudes and beliefs about authority and recovery, integration of lived experience into mental health risks being symbolic rather than transformative.

There are worldwide shortages in the mental health nursing workforce. Transition-to-practice programs are a vital avenue for recruiting and supporting nurses entering the mental health sector. There is little evidence, however, on the psychosocial and work-related outcomes of mental health transition nurses (registered and enrolled) during their transition into this specialty field. The overall aim of this study was to investigate mental health transition nurses' perceived stress, well-being, resilience, work satisfaction, turnover intention and mental health stigma attitudes at 6 months into their program and examine changes in these outcomes between a baseline assessment at 4 weeks into transition and follow-up at 6 months. At follow-up, perceived stress, well-being and resilience scores were moderate, turnover intention was low and work satisfaction of n = 49 nurses was high. Higher work satisfaction predicted greater well-being, less stigmatising attitudes predicted higher resilience and lower work satisfaction predicted increased turnover intention. Only turnover intention significantly increased from baseline to 6 months. Nurses with scores indicating poor well-being (n = 10) had significantly higher perceived stress and turnover intention and lower work satisfaction and stigma. These findings highlight the need for targeted support over the initial transition period to promote nurses' mental health, well-being and retention as they transition into the field. Addressing factors such as work satisfaction and stigma in transition programs may be essential for reducing nurses' stress and turnover intentions, particularly for nurses experiencing reduced well-being.

Adolescents are particularly vulnerable to mental disorders, with over 75% of lifetime cases emerging before the age of 25 years. Yet most young people with significant symptoms do not seek support. Digital phenotyping, leveraging active (self-reported) and passive (sensor-based) data from smartphones, offers a scalable, low-burden approach for early risk detection. Despite this potential, its application in school-going adolescents from general (nonclinical) populations remains limited, leaving a critical gap in community-based prevention efforts.

This study evaluated the feasibility of using a smartphone app to predict mental health risks in nonclinical adolescents by integrating active and passive data streams within a machine learning (ML) framework. We examined the utility of this approach for identifying risks related to internalizing and externalizing difficulties, eating disorders, insomnia, and suicidal ideation.

Participants (n=103; mean age 16.1 years, SD 1.0) from 3 UK secondary schools used the Mindcraft app (Brain and Behaviour Lab) for 14 days, providing daily self-reports (eg, mood, sleep, and loneliness) and continuous passive sensor data (eg, location, step count, and app usage). We developed a deep learning model incorporating contrastive pretraining with triplet margin loss to stabilize user-specific behavioral patterns, followed by supervised fine-tuning for binary classification of 4 mental health outcomes, namely, the Strengths and Difficulties Questionnaire (SDQ)-high risk, insomnia, suicidal ideation, and eating disorder. Performance was assessed using leave-one-subject-out cross-validation (LOSO-CV), with balanced accuracy as the primary metric. Comparative analyses were conducted using CatBoost (Yandex) and multilayer perceptron (MLP) models without pretraining. Feature importance was assessed using Shapley Additive Explanations (SHAP) values, and associations between key digital features and clinical scales were analyzed.

Integration of active and passive data outperformed single-modality models, achieving mean balanced accuracies of 0.71 (0.03) for SDQ-high risk, 0.67 (0.04) for insomnia, 0.77 (0.03) for suicidal ideation, and 0.70 (0.03) for eating disorder. The contrastive learning approach improved representation stability and predictive robustness. SHAP analysis highlighted clinically relevant features, such as negative thinking and location entropy, underscoring the complementary value of combining subjective and objective data. Correlation analyses confirmed meaningful associations between key digital features and mental health outcomes. Performance in an independent external validation cohort (n=45) achieved balanced accuracies of 0.63-0.72 across outcomes, suggesting generalizability to new settings.

This study demonstrates the feasibility and utility of smartphone-based digital phenotyping for predicting mental health risks in nonclinical, school-going adolescents. By integrating active and passive data with advanced machine modeling techniques, this approach shows promise for early detection and scalable intervention strategies in community settings.

The landscape of total shoulder arthroplasty (TSA) is rapidly evolving, with a significant shift toward outpatient procedures. This transition has been supported by enhanced recovery protocols and shorter hospital stays. Key factors for successful outpatient TSA include careful patient selection, focusing on individuals with minimal comorbidities, and preoperative optimization, such as patient education and mental health assessments. Intraoperative considerations like blood loss management, pain control, and surgical efficiency play a crucial role in ensuring positive outcomes. Although challenges remain, including patient selection bias and the need for standardized protocols, ongoing research, innovation in surgical practices, and integration of technology can further enhance the safety and effectiveness of outpatient TSA. Ultimately, with appropriate patient selection and optimization strategies, outpatient TSA can provide comparable outcomes to inpatient procedures, benefiting both patients and health care systems.

Suicide is the second-leading cause of US childhood mortality after 9 years of age. The accurate measurement of pediatric emergency service use for self-injurious thoughts and behaviors (SITB) remains challenging, as diagnostic codes undercount children. This measurement gap impedes public health and prevention efforts. Current research has not established which combination of electronic health record data elements achieves both high detection accuracy and consistent performance across youth populations.

This study aims to (1) compare the detection accuracy of electronic health record-based methods for identifying SITB-related pediatric emergency department (ED) visits: basic structured data (International Classification of Diseases Version 10, Clinical Modification codes, chief concern), comprehensive structured data, clinical note text with natural language processing, and hybrid approaches combining structured data with notes; and (2) for each method, measure variability in detection by youth demographics and underlying mental health diagnosis.

Multiple human experts reviewed clinical records of 3828 pediatric mental health emergency visits (28,861 clinical notes) to a large health system with 2 EDs (June 2022-October 2024). The reviewers used the Columbia Classification Algorithm for Suicide Assessment to label the presence of SITB at the visit. Random forest classifiers were developed using 3 data modalities: (1) structured data (low-dimensional [International Classification of Diseases codes and chief concerns], medium-dimensional [adding Columbia Suicide Severity Rating Scale screening or mental health diagnoses], and high-dimensional [all structured data or augmented case surveillance, aCS]); (2) text data (general-purpose natural language processing, medical text-specific trained natural language processing, and Large Language Model Meta AI-derived scores), and (3) hybrid data (combining aCS with each text approach). Model performance was evaluated using area under the receiver operating characteristic curve (AUROC).

Of the 3828 visits, 1760 (n=1760, 46.0%) were SITB-related. Detection performance improved with dimensionality: low-dimensional (AUROC=0.865), medium-dimensional (AUROC=0.934-0.935), and high-dimensional (AUROC=0.965). Low-dimensional structured (International Classification of Diseases codes and chief concerns) showed high variability in detection, with lower accuracy among preadolescents (AUROC=0.821 vs 0.880 for adolescents); male participants (AUROC=0.817 vs 0.902 for females); and patients with neurodevelopmental (AUROC=0.568-0.809), psychotic (AUROC=0.718), and disruptive disorders (AUROC=0.703). Hybrid modality (aCS+Large Language Model Meta AI) achieved optimal performance (AUROC=0.977), with AUROC ≥0.90 for all 20 demographic and 12/15 diagnostic subgroups.

This cross-sectional retrospective study identified that, relative to diagnostic codes and chief concern alone, hybrid structured-text detection methods improved accuracy and mitigated unwanted detection variability. The findings offer a scaffold for future clinical deployment of improved information retrieval of pediatric suicide and self-harm-related emergencies.

Dysregulations in interoception have been associated with mental health disorders including eating and somatic symptom disorders. The present study addresses the feasibility and acceptability of a novel, behavioural intervention (InMe) in a healthy sample with low, self-reported interoception. The efficacy of the InMe intervention against an active control arm was tested in a randomised controlled trial (RCT) reported elsewhere, while the feasibility and acceptability of InMe were assessed in parallel and are fully reported here. Participants were randomly assigned to the intervention arm (InMe) or active control arm and stratified according to their self-reported gender and a cut-off score from the Eating Disorders Questionnaire (EDE-Q). Feasibility and accessibility measures included self-report scales and questionnaires, assessor checklists and ratings, as well as behavioural and physiological responses. Data was gathered from a total of 102 participants and encompassed trial recruitment and retention rates, the suitability of measurement tools, as well as the feasibility and acceptability of stressors, interventions and other trial procedures. The study found satisfactory feasibility in recruitment procedures, trial measurement tools, and intervention procedures. Participants perceived the intervention as acceptable, though minor adjustments for trial optimisation were identified. Overall, this feasibility study provided promising evidence regarding the acceptability and feasibility of an interoception based intervention in a RCT context. These findings offer valuable insights particularly for the design of future clinical trials for testing the efficacy of this intervention further in clinical populations.

Hepatitis B (HBV) is a chronic viral infection that can lead to cirrhosis, liver failure, and liver cancer, and has a profound impact on the patient's mental health. However, current depression screening mainly relies on self-filled scales and clinical experience, lacking objective and efficient prediction tools. This study aims to construct a risk prediction model for depression in hepatitis B patients based on machine learning, and explore the key features that affect the occurrence of depression, so as to optimize mental health management strategies.

This study used the NHANES database to collect demographic, dietary, physical examination, laboratory test and questionnaire data. The data were standardized and SMOTE oversampling was used to solve the problem of class imbalance. Random Forest (RF) was used for feature screening to identify the top 20 most important predictive features, and five machine learning models (Gradient Boosting, Logistic Regression, AdaBoost, MLPClassifier, LDA) were used for prediction. The model performance was evaluated by AUC (area under the curve), accuracy, recall, precision and F1-score, and ROC curves, calibration curves, and decision curve analysis (DCA) were drawn to evaluate the clinical applicability of the model.

All five machine learning models performed well in the task of predicting the risk of depression in hepatitis B patients, among which MLPClassifier (multi-layer perceptron) performed best, with an AUC of 0.935, a recall of 0.980, and an F1-score of 0.917, which was better than other models. In addition, feature analysis results showed that liver function damage (serum total bilirubin, alkaline phosphatase), electrolyte imbalance (serum potassium ions), chronic inflammation (red blood cell distribution width, lymphocyte count), and socioeconomic factors (poverty-income ratio, race) were important factors affecting the risk of depression in hepatitis B patients.

This study constructed an efficient and objective machine learning model that can be used to predict the risk of depression in patients with hepatitis B, providing a new tool for accurate screening and individualized management. The study revealed the potential mechanisms of physiological, biochemical and socioeconomic factors in the occurrence of depression in patients with hepatitis B, and provided a reference for future mental health intervention strategies.

The IMPULSE trial investigated the effectiveness and implementation of a digital psychosocial intervention (DIALOG+) for people with psychosis in five Southeast European countries. DIALOG+ significantly improved patients' quality of life after four treatment sessions. The process evaluation reported here aimed to assess contextual influences on intervention delivery during the trial, to explain the trial findings and generate hypotheses about mechanisms of action by exploring acceptability from the perspectives of clinicians who delivered it and trial participants who received it, and fidelity (was the intervention delivered and received as planned?).

A mixed-methods process evaluation was conducted in accordance with the published protocol, guided by theoretical frameworks and the Medical Research Council's guidance for complex interventions. To explore the role of context, data were analysed about the participating services, policy documents, and from focus groups with key stakeholders. Semi-structured interviews with clinicians and patients were conducted to explore acceptability. Process data (format and content of sessions) were analysed to assess intervention fidelity. Data analysis included descriptive methods, framework and content analysis, and triangulation.

Several attributes of context related to health services, including resource limitations, funding priorities, reliance on paper records and lack of community support, potentially negatively impacted DIALOG+ acceptability, fidelity and outcomes. Contextual enablers were also identified, including an appetite for change among key stakeholders that can help overcome contextual barriers. Acceptability of the psychosocial intervention was moderate to high and fidelity was high.

Intervention acceptability is likely to have played a key role in ensuring high fidelity, which in turn likely contributed to the intervention's positive impact on patients' quality of life. The high fidelity confirms that the IMPULSE trial findings provide a valid assessment of the intervention as designed. While the identified contextual barriers appear not to have impaired intervention fidelity, acceptability and outcomes, they could pose challenges to the long-term sustainability of the intervention.

Retrospectively registered on 29 March 2021, ISRCTN11913964.

Multimorbidity, the coexistence of 2 or more chronic conditions, is associated with poor well-being. Health coaching apps offer cost-effective and accessible support. However, there is a lack of evidence of the impact of health coaching apps on individuals with multimorbidity.

This study aimed to assess the impact and acceptability of a health coaching app (the Holly Health [HH] app) on the subjective well-being (SWB) of adults with multimorbidity.

This study used an explanatory-sequential mixed methods design, with quantitative secondary data analysis in the first phase and qualitative interviews in the second phase. In the quantitative phase (n=565), pre- and post-SWB (Office for National Statistics' 4 personal well-being questions [ONS4]) scores from existing app users with multimorbidity were analyzed using Bayesian growth curve modeling to assess the impact of HH. In the qualitative phase (n=22), data were collected via semistructured interviews and analyzed using reflexive thematic analysis. Mechanisms of action that supported SWB were categorized using the Multi-Level Leisure Mechanisms Framework.

There was a significant increase in life satisfaction (Coef.=0.71, 95% highest density interval [HDI] 0.52-0.89), worthwhileness (Coef.=0.62, 95% HDI 0.43-0.81), and happiness (Coef.=0.74, 95% HDI 0.54-0.92) and a decrease in anxiety (Coef.=-0.50, 95% HDI -0.74 to -0.25) before and after using the HH app. Overall, 8 acceptable app features activated 5 mechanisms of action, including behavioral, psychological, and social mechanisms. Three additional factors influenced the acceptability of the health coaching app: type of chronic condition, availability of time, and the use of other support tools.

The study demonstrates that health coaching apps could be effective and acceptable support tools for individuals with multimorbidity. This study contributes to understanding why health coaching apps support SWB and could be used to inform the development of future digital health interventions in multimorbidity.

Mental disorders are responsible for a significant proportion of global health burden especially in developing countries. In sub-Saharan Africa optimum care for mental health patients is constrained by frequent drug stock-outs. Patients who are victims of drug stock-outs are compelled to seek coping mechanisms to this challenge. These coping mechanisms may either be adaptive or maladaptive. Consequently, persons living with mental illnesses are prone to experiencing undesirable outcomes. This study purposed to explore coping mechanisms to drug stock-outs among patients seeking care at an outpatient department (OPD) of a national mental healthcare facility in Kampala, Uganda.

This was an observational cross-sectional study. A sample size of 385 was obtained by systematic random sampling. Data was collected using a checklist and a questionnaire. The data was analyzed using SPSS version 29. Numerical variables were presented with means and standard deviations. Categorical variables were presented as frequencies and percentages. The results of the study were presented in tables, pie-charts and graphs.

Less than half of the participants; 164/385(42.60%), had their prescriptions fully filled with an average prescription fulfillment rate of 66.36%. Majority of the patients were victims of drug stock-outs. The most popular coping mechanisms were; out-of-pocket purchase of prescribed drugs from pharmacies, patients returning to hospital on a later date, skipping drug doses and using unprescribed herbal remedies. The commonest undesirable outcomes of coping mechanisms were; worsening of symptoms, insomnia and relapse of signs and symptoms.

Drug stock-outs could have been responsible for low prescription fulfillment rates. This most likely prompted numerous patients to resort to alternative treatment modalities. These unprescribed treatment modalities could jeopardize patient prognosis and overall safety.