Tag: Substance abuse

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Improving family functioning and wellbeing among families of children with disabilities in Rwanda: an application of the SAFE child protection model

BackgroundFamilies of children with disabilities in low-resource settings face heightened challenges to family functioning and wellbeing. Improving a child and caregiver’s environment in these settings requires a broader understanding of the family ecosystem. This study aims to investigate factors that should be considered to enhance family functioning and inform family-centered interventions.MethodologyWe conducted 34 in-depth interviews with 30 caregivers of children with disabilities aged 6–8 years enrolled in a larger cluster randomized trial of the Sugira Muryango parenting intervention. Additionally, six focus group discussions were conducted with 36 community workers, and 6 Early Childhood Developmental (ECD) advocates participants. Guided by the SAFE Model for child protection, content analysis was used to understand threats to child protection and caregivers’ well-being among caregivers of children with disabilities enrolled in Sugira Muryango.ResultsConsistent with the domains outlined in the SAFE Model, our analysis revealed the following threats to child safety: 1) Safety/Freedom from harm, including father’s mental health and substance abuse, abandonment and stigma related to a child’s disability; 2) Access to basic physiological needs and healthcare, including lack of basic necessities (food, utilities and housing), additional needs related to caring for a child with disabilities, caregiver mental health needs, limited access to health insurance and mother illness; 3) Family and connection to others, including the single-parent dilemma, lack of support from family and community, and father abandonment; and 4) Education and economic security, including additional expenses due to the child’s disability and financial stress related to health care costs.ConclusionImproving family functioning and wellbeing among caregivers of children with disabilities in limited resources requires a deep understanding of socioecological forces operating on the family system. These findings provide further evidence of how the threats to child safety highlighted by the SAFE Model can inform how researchers, and policy makers design and implement family-centered interventions tailored to children with disabilities in low-resource settings.
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Screening and Monitoring of Mental Health in Educational Settings: Programme for Detection, Psychoeducation and Self-help

Conditions: Anxiety; Depression Disorder; Bullying; Addiction; Suicide; Self Harm; Psychosis; Eating Disorders

Interventions: Other: program for the detection, referral, and psychoeducation in adolescents

Sponsors: Instituto de Investigación Sanitaria de la Fundación Jiménez Díaz; Hospital Universitario Rey Juan Carlos; Hospital Universitario Infanta Elena; Universidad Carlos III de Madrid; Centro de Investigación Biomédica en Red de Salud Mental

Completed
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Altered long-range visual hierarchical connectivity in internet gaming disorder: reduced V1–LOC connectivity and its association with addiction severity

BackgroundNeuroimaging studies of internet gaming disorder (IGD) have primarily focused on reward-related and executive-control networks. However, sensory processing of addiction-related cues, particularly visual processing, may also contribute to IGD. Whether the intrinsic hierarchical organization of the visual system is altered remains unclear.MethodsThe study included 35 individuals with IGD and 30 healthy controls (HCs), with an overall age range of 9–33 years. The sample was predominantly composed of adolescents. An a priori three-level visual hierarchy model comprising the primary visual cortex (V1), lingual gyrus, and lateral occipital cortex (LOC) was constructed. Functional connectivity (FC) was calculated for the V1–Lingual, Lingual–LOC, and V1–LOC pathways. A hierarchical integration index (HII), derived from V1–LOC and V1–Lingual connectivity, was calculated to assess long-range relative to local visual coupling. Group differences were assessed using MANCOVA with follow-up univariate analyses and FDR correction. Within the IGD group, associations between IAT scores and all visual hierarchical measures were examined using Pearson and covariate-adjusted partial correlations with FDR correction.ResultsV1–LOC connectivity was significantly reduced in the IGD group compared with HCs (HC: 0.364 ± 0.132; IGD: 0.130 ± 0.189; F(1,59) = 30.809; FDR-corrected p < 0.001; partial η2 = 0.343). V1–Lingual and Lingual–LOC connectivity did not show significant group differences, although their effect sizes were small-to-moderate (Cohen’s d = 0.32 and 0.40, respectively). The HII was numerically lower in IGD but did not reach statistical significance (t = 1.90, p = 0.062; Cohen’s d = 0.46). Within the IGD group, lower V1–LOC connectivity was associated with higher IAT scores (r = −0.479, FDR-corrected p = 0.016), and this association remained significant after controlling for age, sex, education, and mean FD (partial r = −0.447, FDR-corrected p = 0.048).ConclusionsIGD is associated with altered long-range hierarchical connectivity within the visual system, particularly between early visual cortex and higher-order LOC. These findings provide a sensory-level perspective complementing reward- and control-centered accounts of IGD.
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A Mobile App for Student Veterans With Co-Occurring Academic and Psychiatric Concerns: Pilot Usability and Feasibility Study

<strong>Background:</strong> Over 600,000 student veterans use military benefits to pursue higher education annually, with 75% enrolled as full-time students. Advancing their education is an important step toward improving their employment, financial, and social opportunities. However, student veterans also experience high rates of co-occurring psychiatric concerns like substance use disorder (SUD), posttraumatic stress disorder (PTSD), or depression. For these students, additional psychoeducation, support, and guidance may be required. To support their goals, a mobile app with targeted content for student veterans managing common psychiatric concerns could be particularly helpful. <strong>Objective:</strong> The primary aim of this pilot study was to iteratively revise and assess usability, acceptability, and feasibility of the VetEd mobile app and study procedures. The secondary aim was to explore preliminary changes from preapplication to postapplication use in academic functioning and community reintegration. <strong>Methods:</strong> This study used a mixed methods approach; pilot usability testing and iterative revisions were completed across 3 waves of participants (wave 1=4; wave 2=4; wave 3=5; total n=13). Usability was measured using the System Usability Scale (SUS), and acceptability was measured using the Acceptability E-Scale (AES). App use feasibility was collected through app engagement data from the 4 weeks of mobile app use. Study feasibility was assessed by rates of recruitment, retention, and survey completion. Secondary, exploratory measures to assess potential VetEd use outcomes included academic self-efficacy, perceived ability to cope with educational barriers, and community reintegration. Participants were also given the opportunity to complete an exit interview to provide additional feedback. <strong>Results:</strong> Study feasibility was high, with greater than projected rates of recruitment (4-5 per month), retention (13/13, 100%), and survey completion at all 3 time points (100%, 85%, and 100%, respectively). Overall, 85% (11/13) of both usability and acceptability scores were above the predefined benchmarks. Completion of assigned VetEd activities was high, with participants completing, on average, 90.8% of requested app activities. Secondary exploratory outcomes in academic self-efficacy, coping with educational barriers, and community reintegration were nonsignificant but showed feasibility for use in future fully powered randomized VetEd trials. Exit interviews highlighted that VetEd was easy to use, included pertinent information for both academic and mental health functioning, and had vital information on connecting to additional resources. <strong>Conclusions:</strong> Pilot findings suggested that the VetEd mobile app is usable and acceptable as an asynchronous resource for student veterans managing psychiatric concerns. Exit interviews suggested that veterans found app content useful, with relevant information and resources to support their academic functioning. Additional research is needed to assess its effectiveness, implementation, and impact on student veterans nationwide. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT05344092: https://clinicaltrials.gov/study/NCT05344092
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Large language models for late-life depression: a blinded benchmark of clinical safety, geriatric appropriateness, and triage

BackgroundGeneral-purpose large language models are increasingly used by patients and caregivers to obtain mental health information and guidance about when professional care is required. In late-life depression, broadly accurate information may nevertheless be unsafe when cognitive change, multimorbidity, frailty, polypharmacy, self-neglect, caregiver dependence, or suicide risk is not adequately recognized. This study compared the clinical accuracy, safety, geriatric-specific appropriateness, triage performance, and response consistency of three large language models when answering patient- and caregiver-centered questions about late-life depression.MethodsWe conducted a blinded, paired benchmarking study using 90 questions covering six geriatric psychiatry domains and equally distributed across low-, moderate-, and high-risk strata. Each question was submitted independently to GPT-5.5 Instant via ChatGPT, Gemini 3.5 Flash via Gemini, and Seed2.0 Pro via Doubao, generating 270 primary-round responses. A stratified subset of 30 questions was resubmitted in separate conversations to assess test-retest consistency, yielding 360 responses overall. Two psychiatrists independently evaluated anonymized outputs against prespecified item-specific reference standards, with clinically important disagreements adjudicated by a third senior psychiatrist. The primary outcome was the proportion of clinically acceptable responses, defined using accuracy, clinical safety, geriatric appropriateness, warning-sign recognition, and triage criteria.ResultsClinically acceptable responses were generated for 78.9% of questions by ChatGPT, 72.2% by Gemini, and 60.0% by Doubao (overall P<0.001). The difference between ChatGPT and Gemini was not statistically significant, whereas both outperformed Doubao. This model ranking remained unchanged under alternative core-safety and more stringent optimal-response definitions. Complete geriatric-specific appropriateness was achieved in 64.4%, 55.6%, and 43.3% of responses, respectively. Major safety errors occurred in 5.6% of ChatGPT responses, 10.0% of Gemini responses, and 16.7% of Doubao responses (raw P = 0.015; FDR-adjusted q=0.023). Performance declined substantially with increasing clinical risk. In exploratory post hoc caregiver-centered analyses, clinical acceptability was 76.7% for ChatGPT, 70.0% for Gemini, and 53.3% for Doubao, while explicit caregiver-directed action was present in 80.0%, 70.0%, and 53.3% of responses, respectively. Among high-risk questions, clinically acceptable response rates were 63.3%, 50.0%, and 36.7%, while under-triage occurred in 16.7%, 26.7%, and 36.7% of responses, respectively. Test-retest clinical consistency was highest for ChatGPT (90.0%), followed by Gemini (83.3%) and Doubao (73.3%).ConclusionsThe three models answered many late-life depression questions accurately and safely, but none demonstrated consistently reliable performance across complex or high-risk scenarios. Clinically important weaknesses involved geriatric-specific interpretation, recognition of indirect risk signals, crisis-response completeness, under-triage, and response stability. General-purpose large language models may support selected low-risk educational tasks but should not independently guide emergency triage, medication changes, suicide-risk management, or other safety-critical decisions in older adults.
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Tool or Companion? Reframing Conversational AI to Prevent Psychological Harm

People increasingly turn to conversational AI for companionship, emotional support, and well-being, using both purpose-built companion apps, such as Replika and Character.AI, and general-purpose assistants, such as ChatGPT and Claude. While some evidence suggests potential benefits, including short-term reductions in loneliness and mood improvement, several adverse outcomes have been reported in both clinical and nonclinical populations, including emotional dependence, exacerbation of symptoms, and self-harm. The fluent and apparently empathic responses from these models lead users to engage with them not only as tools but also as if they were social entities. This framing is conceptually misleading and may pose risks across different user profiles, particularly for vulnerable individuals. Drawing on research in AI, psychiatry, psychology, and network science, we highlight mechanisms through which emotional reliance develops and the boundary between tool and companion erodes. Design choices that evoke personality and warmth encourage users to anthropomorphize these systems. Simulated empathy, generated through probabilistic language patterns rather than genuine emotional experience, creates a structurally asymmetric interaction, in which the user discloses and the system responds, but without reciprocity, vulnerability, or accountability. Overvalidation and sycophancy can reinforce maladaptive cognitions, delusional ideation, and distorted perceptions of reality, as they tend to reinforce people’s beliefs, even at the expense of the accuracy of models’ responses. These mechanisms are not incidental: they emerge from alignment procedures that reward responses perceived as warm and empathic. The result is a self-reinforcing feedback loop between the model and the user that may amplify maladaptive beliefs, delusional ideation, and emotional distress, even in those who engage for largely functional purposes. Understanding these dynamics requires an examination of both what these agents can do—considering their technical limitations and implementations—and what humans believe they can do, including social and psychological impacts. We argue that conversational AI should be treated primarily as a tool supporting human systems rather than as a substitute for human relationships. Perhaps more importantly, reviewing the current hype surrounding AI interactions can help reformulate a paradigm that contributes to human well-being and societal value, while minimizing misconceptions, maladaptive interactions, or social disintegration.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/31acb2c2de9a6960a54052d3939a044a" />

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Mobile Application-Based Interventions for Cannabis Use in Young Adults With First Episode Psychosis

Conditions: First Episode Psychosis (FEP); Psychosis; Schizophreni-form Disorder; Dependence Addictive

Interventions: Other: CHAMPS; Other: I Can Change

Sponsors: Centre hospitalier de l’Université de Montréal (CHUM); Integrated University Health and Social Services Center of the Capitale-Nationale; Nova Scotia Health Authority; Centre Intégré de Santé et de Services Sociaux de la Montérégie-Centre; Centre Integre Universitaire de Sante et Services Sociaux du Nord de l’ile de Montreal; Ottawa Hospital Research Institute

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Development and psychometric evaluation of the AI emotional engagement scale

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into daily life, the underlying mechanisms of emotional interactions between users and AI have emerged as a critical research topic in the field of human-computer interaction (HCI). Existing studies have predominantly adopted traditional technology acceptance models or satisfaction scales, which lack targeted applicability in capturing unique features of AI interactions, such as parasocial relationships and emotional dependence. The present study aims to develop a self-report instrument, the AI Emotional Engagement Scale (AEES), and examine its psychometric properties for assessing users’ level of AI Emotional Engagement with AI systems.MethodsThis study was conducted based on Norman’s three-level theory of emotional design. Study 1 (N = 24) extracted core information from interview data using qualitative analysis, and generated an initial item pool through multiple rounds of expert review. Study 2 (N = 103) conducted a pre-test of the initial items to examine indicators including item quality and relevance, optimized the item design, and developed the formal item set. Study 3 (N = 924) verified the factor structure of the AEES via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), conducted reliability and validity tests of the scale, and finalized the factor structure of the 28-item, three-dimensional scale.ResultsResults of EFA indicated that AI Emotional Engagement consists of three dimensions: the visceral level, behavioral level, and reflective level, which was consistent with the theoretical framework, with a cumulative variance contribution rate of 68.32%. Results of CFA demonstrated that the three-factor model had a good fit (χ²/df = 3.765, CFI = 0.91, TLI = 0.90, RMSEA = 0.077, SRMR = 0.0496). The overall AEES and its three dimensions exhibited good internal consistency reliability, with reliability coefficients ranging from 0.90 to 0.97.ConclusionThe AEES has favorable psychometric properties, and can serve as a reliable instrument for researchers and practitioners to assess users’ AI Emotional Engagement with AI systems.