BackgroundInternalized stigma negatively impacts recovery outcomes, quality of life, and self-concept among individuals with psychiatric diagnoses. Group-based psychosocial interventions have been proposed as effective stigma-reduction strategies, but their impact across diverse populations remains under-evaluated.ObjectiveThis systematic review synthesizes global evidence on the effectiveness of group-based interventions in reducing internalized stigma in adult psychiatric populations, with a focus on studies using the Internalized Stigma of Mental Illness (ISMI) scale.MethodsFollowing PRISMA 2020 guidelines, we searched PubMed, PsycINFO and Web of Science, and additionally screened full-text platforms (SpringerLink, ScienceDirect, SAGE Journals, and Wiley Online Library), for studies published between 2003 and 2025. Inclusion criteria required adult psychiatric populations, group-based interventions, and internalized stigma as a primary outcome. Study selection, risk of bias assessment, and data extraction were performed independently by two reviewers (US and GOC).ResultsTen studies [n= 1,088], across five countries, met inclusion criteria, including randomized controlled trials and pre-post designs. Most studies reported significant reductions in ISMI scores post-intervention, particularly in the domains of stereotype endorsement and social withdrawal. Culturally adapted interventions in China, Poland, and Spain demonstrated feasibility and impact, though subscale reliability varied regionally.ConclusionGroup-based psychosocial interventions may help reduce internalized stigma in psychiatric populations within an ISMI-based evidence base. The ISMI scale is, to this day, among the most frequently used instrument, though cultural adaptation of subscales such as stigma resistance remains a concern.
Machine learning-based predictive factor analysis of depression among Chinese adolescents
IntroductionAdolescent depression has emerged as a critical global public health concern, with rising prevalence in China posing severe threats to psychological development and social adaptation. Traditional statistical methods face limitations in capturing complex non-linear relationships and interactions among influencing factors, while machine learning algorithms offer advantages in predictive modeling of mental health disorders.ObjectiveThis study aimed to: (1) compare the performance of seven ML algorithms in classifying low and high depression risk groups among Chinese adolescents; (2) identify key predictive factors from demographic, personality, and PGI-related variables; (3) explore non-linear relationships and interactive effects between critical factors; and (4) explore preliminary threshold values for key factors as potential references for risk identification.MethodsA total of 559 Chinese adolescents completed assessments of demographic characteristics, Big Five personality traits, personal growth initiative, and depression symptoms. Model performance was compared using Friedman tests and Nemenyi post-hoc tests appropriate for correlated cross-validation data. Seven ML algorithms were trained and optimized using 5-fold cross-validation. Feature importance was analyzed via traditional metrics and SHAP values, and SHAP interaction effects were tested using permutation tests. Threshold analysis was conducted using the Youden’s J statistic.ResultsLightGBM outperformed other models with an AUC of 0.834, achieving balanced accuracy, sensitivity, and specificity. Neuroticism emerged as the most robust predictor across all models, followed by proactive change, agreeableness, extraversion, and growth resilience. Demographic factors showed minimal predictive power. SHAP permutation tests confirmed significant interactions between neuroticism and proactive change and between proactive change and agreeableness, whereas no significant interaction was found between neuroticism and agreeableness. Preliminary thresholds were identified for key factors within this sample.ConclusionML algorithms, particularly lightGBM, effectively identify adolescent depression risk, with personality traits and PGI serving as core predictive factors. The findings highlight the value of integrating multi-dimensional variables in depression prediction and provide preliminary references for early intervention. Given the cross-sectional design and lack of external validation, conclusions regarding generalizable cutoffs and causal inference should be made with caution. Targeted strategies focusing on reducing neuroticism and enhancing proactive growth behaviors may mitigate depression vulnerability in Chinese adolescents.
Understanding the workplace needs of autistic adults in Singapore: insights to inform inclusive AI support
IntroductionAutistic adults face persistent challenges in obtaining and sustaining meaningful employment. Despite growing attention to workplace inclusion, research on autistic adults’ employment experiences in non-Western contexts remains scarce. The potential of emerging technologies, such as large language models (LLMs), to support workplace integration is also still largely unexplored, yet off-the-shelf models may reflect neurotypical norms that risk reinforcing masking or overlooking neurodivergent needs. This qualitative, participatory study therefore centered autistic adults’ perspectives to understand workplace experiences and identify potential LLM affordances that could facilitate integration.MethodsTwenty autistic adults with at least 3 months of work experience were recruited in Singapore. Data was collected through semi-structured group discussions. Using reflexive thematic analysis, we identified workplace challenges, needs, and potential support LLMs can provide. We had a multidisciplinary team of autistic and non-autistic researchers, and autistic perspectives actively shaped the design, conduct, and interpretation of the research.ResultsTwo overarching themes emerged: (1) assumed neurotypicality of the workplace, evident in work processes and social participation, and (2) need for workplace inclusivity, supported through both individual accommodations and systemic change, including identity-affirming support, alignment of work design and tools with neurodivergent working styles, empowered access to supports and accommodations, and shared responsibility for workplace integration. Potential LLM functionalities involve supporting executive functioning, encouraging self-reflection, and fostering mutual understanding between autistic employees and their coworkers.DiscussionWorkplace barriers for autistic employees often stem from assumed neurotypical norms rather than individual deficits. Participants reported challenges related to ambiguous work processes, implicit social expectations, and executive functioning demands, which reflect a mismatch between workplace structures and neurodivergent ways of working. Crucially, inclusivity cannot rely solely on individual accommodations; meaningful workplace inclusion requires systemic change. Designing LLM tools that align with neurodivergent working styles can complement systemic inclusivity efforts and empower autistic employees. Implications and future directions are discussed.
Effects of an Eye-Tracking Digital Serious Game on Cognitive Function in Mild Cognitive Impairment: Pilot Intervention Study
<strong>Background:</strong> Cognitive decline in aging populations underscores the need for early interventions in mild cognitive impairment (MCI), where pharmacological treatments show limited benefit. Eye-movement metrics serve as sensitive markers of cognitive deficits in MCI, and digital programs integrating these tasks offer scalable, data-driven training approaches. <strong>Objective:</strong> This study aimed to evaluate the effectiveness of a digital cognitive training program incorporating eye-movement tasks in individuals with MCI, and to determine whether eye-movement indicators can serve as objective markers of cognitive improvement. <strong>Methods:</strong> A total of 12 participants aged 60-85 years with MCI (Korean version of the Montreal Cognitive Assessment [K-MoCA] score of ≤22) completed baseline and postintervention assessments using the K-MoCA and Mini-Mental State Examination-Korean version (MMSE-K). Longitudinal changes in visuospatial attention and oculomotor performance were examined using Spearman correlations across sessions, and pre-post comparisons of eye-tracking metrics were conducted to assess training-related improvements. <strong>Results:</strong> Cognitive scores improved significantly, with K-MoCA increasing by 1.5 points (from mean 20.3, SD 1.1 to mean 21.8, SD 1.7; <i>P</i>=<i>.</i>004; Cohen <i>d</i>=1.38) and MMSE-K by 1.3 points (from mean 21.9, SD 2.0 to mean 23.2, SD 2.2; <i>P</i>=<i>.</i>002; Cohen <i>d</i>=1.29). Fixation duration decreased (<i>r</i>=0.248; <i>P</i>=<i>.</i>003), and saccade velocity increased (<i>r</i>=0.258; <i>P</i>=<i>.</i>002), indicating enhanced visual processing efficiency and faster attentional shifts, whereas fixation count and saccade amplitude showed no consistent changes. In addition, saccade duration decreased by 21.72 ms, and saccade velocity increased by 114.54 °/s. <strong>Conclusions:</strong> Digital cognitive training yielded measurable gains in visuospatial attention and oculomotor efficiency in MCI, with optimized fixation and saccade patterns indicating enhanced attentional control and information processing. These findings support eye-movement metrics as sensitive indicators of cognitive change and highlight digital interventions as scalable, noninvasive tools for cognitive support in aging populations.
Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
<strong>Background:</strong> Artificial intelligence (AI)–based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. <strong>Objective:</strong> This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. <strong>Methods:</strong> ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA’s classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. <strong>Results:</strong> ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. <strong>Conclusions:</strong> In this initial validation study, ASTRA reliably identified multiple forms of mental health–related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
Barriers and Facilitators for the Implementation of an Online Portal in Hospital Mental Health Care: Implementation Study
Background: In recent years, digital patient portals have become an increasingly common feature of care in various medical fields. Despite growing scientific evidence of their effectiveness and the benefits they offer to patients and caregivers, their implementation, especially in hospital mental health settings, lags behind expectations. Objective: The study aimed to identify the barriers and facilitators to implementing a patient portal in a public mental health hospital setting in Germany. Moreover, it aimed to develop recommendations for implementing a patient portal. Methods: Three psychiatric clinics in the early stages of implementing an online portal for patients participated in this implementation study. We assessed objective usage data (log data from the patient portal) and performed qualitative interviews with professionals and questionnaire surveys with both patients and professionals. We combined the results to develop generic recommendations for the implementation of patient portals in a mental hospital setting using a 2-stage Delphi method with a group of professionals and patients. Results: Portal log data from 71 patients indicated variation in the use of the portal functions. On average, users logged in 9.5 (SD 14.9) times (median 4, IQR 2-7 times). The variability in the number of logins per patient, ranging from 1 to 72, indicated a high variance in the frequency of use. On average, the portal was used for 47 (SD 59) days (median 27, IQR 2-62 days). Questionnaire data from 27 patients showed satisfaction with the portal and elucidated perceived barriers to usage. Qualitative interview data from 15 professionals revealed patient-related, professional-related, organizational, structural, and technical facilitators and barriers to the implementation process. We developed 10 actionable recommendations for the implementation of digital patient portals in psychiatric hospitals, which were rated by an expert group on different dimensions. Conclusions: To our knowledge, this is the first implementation study in a German mental health hospital setting that provides experience-based recommendations for advancing the implementation of digital patient portals in hospital mental health care. The next steps will include the analysis of a larger number of users and functions, which will help to specify recommendations for different target groups and settings. Trial Registration: German Clinical Trials Register DRKS00036894; https://drks.de/search/en/trial/DRKS00036894
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Paddy Pimblett on Mental Health Fitness
MMA Champion Paddy Pimblett’s candid message to young people
In recognition of Mental Health Awareness Month, the Child Mind Institute has launched the Mental Health Fitness campaign — a national call to action highlighting the importance that caring for one’s mental health is just as important as physical health.
Paddy Pimblett, an English MMA champion, opens up about relying on training to strengthen his body and steady his mind. His message goes beyond sports: structure, purpose, and setting achievable goals can be transformative, especially for young people struggling to feel “good enough.” By speaking candidly about his mental health, he offers a version of strength rooted in resilience, honesty, and showing up for yourself every day.
“I’ve suffered with mental health problems and depression, and I’ve had to get through them. Any kid who’s out there and feels they’re not good enough, just like I did, get some structure in your life. Start doing something where you’ve got a goal at the end of it, so you’re striving to be the best at something. That’s what pushes me on every day.”
About Paddy Pimblett
Paddy Pimblett is an English professional mixed martial artist known for his rising success in the UFC lightweight division. He was named the 2022 Breakthrough Fighter of the Year at the World MMA Awards. Known as Paddy “The Baddy”, Pimblett has built a large following both inside and outside of the Octagon for his entertaining fighting style and his openness about personal struggles and emotional well-being.
About Mental Health Fitness
For decades, we’ve understood that physical fitness doesn’t just happen — it takes skills, regular practice, and a supportive environment. The same is true for mental health. Developed by experts at the Child Mind Institute for three different age groups, our Mental Health Fitness guides have been used by more than 1.8 million students, caregivers, and educators to build emotion regulation skills and resilience. Whether your child is 5 or 15, struggling or thriving, they can learn these skills. And you can practice alongside them. Learn more at Mental Health Fitness.
Related Resources
- How to Help Children Calm Down
- What Is Co-Regulation?
- How to Model Healthy Coping Skills
- Raising Confident, Independent Children
The post Paddy Pimblett on Mental Health Fitness appeared first on Child Mind Institute.
Stronger associations of affectivity than social leisure activities with cognitive impairment: a 10-year trajectory study of Chinese older adults
BackgroundBoth positive and negative affectivity (PNA) and social leisure activities (SLA) are significantly associated with cognitive function, yet their relative strength of association with long-term cognitive decline remains unclear. Exploring this issue has significant implications for designing precise and effective cognitive health promotion programs in the future.MethodsThe data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Our analysis included 1,847 older adults (≥65 years) without diagnosed dementia at baseline, who were followed from 2008 to 2018. Using Group-Based Trajectory Models (GBTM), participants were classified based on their PNA and SLA trajectories to identify distinct subgroups. Binomial logistic regression analyzed associations between these groups and cognitive function, with Likelihood Ratio and Wald Tests comparing their relative strengths of association.ResultsThe “High-rapid-increasing PNA” group exhibited a 264% higher risk of cognitive impairment compared to the “Low-stable-increasing PNA” group (OR = 3.64, 95% CI: 2.92, 4.52). The “Low-stable SLA” group was associated with increased cognitive impairment risk (OR = 1.66, 95% CI: 1.32, 2.08). PNA demonstrated a stronger association with cognitive function than SLA [Likelihood Ratio Test: Δχ²(1) = 137.37, p < 0.001]. However, a formal test for multiplicative interaction was not statistically significant (OR = 0.88, 95% CI: 0.57, 1.35).ConclusionPNA demonstrated a stronger association with cognitive function in older adults than SLA. Affective factors may be a critical, and potentially underutilized, target for cognitive health interventions in aging populations.
Correlation of non-suicidal self-injury with SLC6A4 promoter DNA methylation in children and adolescents with bipolar depression
PurposeTo investigate the association between non-suicidal self-injury (NSSI) and DNA methylation of the SLC6A4 promoter region in individuals diagnosed with bipolar depression.Patients and methodsA total of 48 children and adolescent patients meeting ICD-10 criteria for bipolar disorder with current depressive episodes (Hamilton Depression Rating Scale-24 score >20) were recruited, and stratified into two groups according to the presence or absence of non-suicidal self-injurious (NSSI) behavior(NSSI group and non-NSSI group). Negative life events were evaluated using the Adolescent Self-Rating Life Events Checklist (ASLEC). The methylation levels of five CpG sites within the SLC6A4 promoter region were determined by pyrosequencing, and mRNA expression was assessed by quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsIn 48 adolescents with bipolar depression, the two groups were comparable in age, gender, education, only-child status, family history of mental illness, and disease course (all p > 0.05). Compared with the non-NSSI group, the NSSI group showed significantly lower CpG2 methylation at the SLC6A4 promoter, higher SLC6A4 mRNA expression, and higher ASLEC scores. The other four CpG sites showed no significant differences. Spearman’s correlation revealed a strong negative association between CpG2 methylation and mRNA expression (ρ = -0.547, p < 0.001). Two logistic regression models both fit the data well. Higher CpG2 methylation was a protective factor against NSSI (OR = 0.131, p = 0.021), while higher mRNA expression was a risk factor (OR = 10.957, p = 0.015). ASLEC scores and interaction terms were not significant.ConclusionThese preliminary findings suggest that lower CpG2 methylation and higher SLC6A4 mRNA expression are each independently associated with NSSI in adolescents with bipolar depression. Stressful life events were higher in the NSSI group, but did not significantly moderate these associations in the current sample. Given the small sample size and cross-sectional design, the results should be viewed as exploratory and require replication in larger studies.
Determinants of antipsychotic prescription in women detainees admitted to an acute forensic psychiatric unit
BackgroundWomen represent a small proportion of the global prison population but carry a disproportionate burden of mental illness. Evidence indicates high rates of psychotropic medication use among women in prison, often independently of the clinical diagnosis. Antipsychotics, particularly second-generation (SGAs) are widely prescribed in forensic settings. Data focusing on the prescription of these agents to women detainees are still scarce.ObjectivesThis study aimed to investigate whether socio-demographic, clinical and forensic characteristics determine SGA prescription to women admitted in an acute forensic psychiatric ward located in prison.MethodsWe conducted a retrospective study of 166 women admitted between 2014 and 2023 to the sole acute forensic psychiatric unit for detainees in French-speaking Switzerland. Among them, 128 cases received second generation antipsychotic medication during their hospital stay. Sociodemographic data included age, nationality, educational attainment and primary spoken language. Criminological variables included types of offense and detention. Clinical variables included psychiatric outpatient and inpatient history prior to conviction, total number of stays during the study period, main diagnosis, presence of substance use disorder and personality disorders. Psychotropic prescriptions were analyzed with conversion of antipsychotic doses into chlorpromazine equivalents. Regression analyses, including LASSO models were used to identify variables associated with antipsychotic dosage.ResultsPsychotropic use was very high with more than two-thirds receiving two or more psychotropic agents. SGAs were prescribed in 77.1% of cases (128 out of 166 cases), while psychotic disorders were diagnosed in only 28.3% indicating frequent off-label use. Regression analyses showed that higher antipsychotic doses were associated with previous psychiatric history, more inpatient stays and court-ordered treatments. Cluster A personality disorders were associated with lower antipsychotic doses.ConclusionsOur findings reveal an extremely high rate of psychotropic use with very frequent off-label prescription for antipsychotics in acute forensic psychiatry wards. Moreover, they show that clinical variables and not demographic and criminological factors determine the use of antipsychotics in this setting.