Tag: Substance use

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Trauma-informed psychological support alongside long-acting injectable buprenorphine: clients’ perspectives on the Buvidal psychological support service (BPSS) pilot in Wales

BackgroundOpioid use disorder (OUD) is associated with persistent drug use, high relapse rates, and significant morbidity, mortality, and psychosocial harm. While Long-Acting Injectable Buprenorphine (LAIB), marketed as Buvidal, offers improved pharmacological management over traditional opioid substitution therapy (OST), the mental clarity provided by LAIB often surfaces unresolved trauma and mental health needs. Integrating trauma-informed psychological care alongside pharmacological intervention is increasingly recognized as critical to optimize recovery outcomes for individuals with co-occurring psychiatric co-morbidities and substance use disorder.ObjectiveThis evaluation aimed to explore client experience of the Buvidal Psychological Support Service, a tiered trauma-informed psychological service, piloted to support individuals with OUD receiving LAIB in Wales, UK.MethodsQualitative data were derived from nine semi-structured interviews with BPSS service users, exploring their experiences, perceived impacts, and recovery journeys through reflexive thematic analysis.ResultsFindings indicated the transition to LAIB was often transformative but exposed trauma and emotional difficulties previously masked by substance use, making the availability of timely psychological support critical for maintaining abstinence and wellbeing. Clients valued the BPSS’s rapid access, continuity, person-centered care and non-judgmental ethos, reporting enhanced agency, emotional regulation, and social reintegration. Relapse prevention was strongly linked to psychological support alongside pharmacological treatment.ConclusionThe findings suggest that participants experienced BPSS as a valuable adjunct to LAIB, particularly where increased mental clarity brought trauma-related and emotional difficulties into focus. The pilot highlights the potential value of integrating trauma-informed psychological support within LAIB pathways, while further evaluation is needed to assess transferability and longer-term outcomes.
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Relationship between loneliness and self-efficacy: the mediating role of internet addiction and the moderating role of sleep quality

IntroductionThis study aimed to examine the association between loneliness and self-efficacy among adolescents and to investigate whether internet addiction mediated this association and whether sleep quality moderated the direct and indirect pathways. Although previous research has demonstrated significant associations between loneliness and both general and social self-efficacy, the behavioral mechanisms underlying these associations and the conditions under which their strength may vary remain insufficiently understood.MethodsA cross-sectional survey was conducted among 1,323 students in Grades 7–9 from two schools in Baotou, Inner Mongolia, China. Participants completed the UCLA Loneliness Scale, the General Self-Efficacy Scale, the Internet Addiction Diagnostic Questionnaire, and the Athens Insomnia Scale. A moderated mediation model was used to examine the mediating role of internet addiction and the moderating role of sleep quality in the association between loneliness and self-efficacy.ResultsLoneliness was negatively associated with self-efficacy. Internet addiction partially mediated the association between loneliness and self-efficacy. In addition, sleep quality moderated the direct association between loneliness and self-efficacy and the first stage of the indirect pathway from loneliness to internet addiction. Specifically, the associations of loneliness with self-efficacy and internet addiction varied across different levels of sleep quality.DiscussionThese findings provide a more comprehensive understanding of the mechanisms linking loneliness to adolescent self-efficacy by identifying internet addiction as an important behavioral mediator and sleep quality as a moderating factor. The findings highlight the importance of considering problematic Internet use and sleep quality when developing interventions aimed at promoting adolescents’ psychological adjustment, self-efficacy, and well-being.
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Methamphetamine-withdrawal catatonia resolved with mixed amphetamine salts: a case report

Catatonia is a well-recognized psychiatric emergency with an evolving understanding of its underlying pathophysiology. However, withdrawal-associated catatonia remains poorly characterized. Here we describe a 32-year-old man with symptoms consistent with methamphetamine-withdrawal catatonia, a previously undescribed etiology, which resolved only after treatment with mixed amphetamine salts. The patient remained catatonic (Bush-Francis Catatonia Rating Scale [BFCRS] >10) for four months despite 38 sessions of electroconvulsive therapy, lorazepam up to 24 mg daily, and multiple augmentation strategies. An extensive medical and neurologic workup excluded alternative causes. Following initiation of mixed amphetamine salts, his BFCRS decreased from 15 to 6 within 48 hours, with sustained resolution and no recurrence of psychosis despite discontinuation of antipsychotics. This case suggests that methamphetamine withdrawal may represent an underrecognized cause of catatonia and that dopaminergic augmentation may have a therapeutic role in refractory cases.
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Predicting multiple mental health outcomes in adolescents using explainable machine learning models

IntroductionAdolescent mental health problems, including depression, anxiety, and stress, are an increasing public health concern, yet the factors associated with different mental health outcomes may vary across domains. This study investigated shared and outcome-specific predictive features of depression, anxiety, perceived stress, and psychological well-being using explainable machine learning models.MethodsA cross-sectional study was conducted among 1,088 adolescents aged 13–18 years recruited from secondary schools in Wuhan, China, using multistage cluster sampling. Participants completed validated measures of emotional dysregulation, loneliness, social media addiction, self-esteem, sleep quality, academic stress, family support, physical activity, and mental health outcomes. Four algorithms—linear regression, support vector regression, Random Forest, and XGBoost—were trained using an 80/20 train-test split with five-fold cross-validation, and model performance was evaluated using test-set R², RMSE, and MAE. SHapley Additive exPlanations (SHAP) were used to examine feature contributions. To minimize target leakage, outcome-specific feature sets were used, with PSQI and RSES excluded from the depression model because of direct or substantial conceptual overlap with PHQ-9 content, and PSQI excluded from the well-being model because of overlap with WHO-5 content.ResultsXGBoost showed the strongest out-of-sample predictive performance across all four outcomes, explaining 49% of the variance in depression (R² = 0.49, 95% CI: 0.44–0.53; RMSE = 3.52; MAE = 2.81), 55% in anxiety (R² = 0.55, 95% CI: 0.50–0.59; RMSE = 3.04; MAE = 2.47), 60% in perceived stress (R² = 0.60, 95% CI: 0.56–0.64; RMSE = 3.71; MAE = 2.87), and 50% in psychological well-being (R² = 0.50, 95% CI: 0.45–0.54; RMSE = 3.38; MAE = 2.68).DiscussionEmotional dysregulation and loneliness were consistently among the most influential features, while academic stress, family support, social media addiction, self-esteem, and sleep quality showed outcome-specific contributions. SHAP rankings for the depression model were stable across five-fold cross-validation, with emotional dysregulation and loneliness consistently occupying the highest ranks.
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Problematic Reliance on Generative AI in an Anxious Young Adult: Case Report

Generative Artificial Intelligence (AI) tools are increasingly integrated into daily life, offering cognitive support across domains such as writing, decision-making, and social interpretation. While beneficial, excessive reliance may contribute to diminished confidence in one’s own thinking. This report describes a woman in her mid-20s with Generalized Anxiety Disorder and Major Depressive Disorder who developed a pattern of functional dependence on generative AI tools- specifically ChatGPT. She historically had strong social, academic and occupational functioning. Psychiatric consultation did not suggest the presence of a personality disorder, and although she was a high-achieving individual, clinically significant perfectionistic traits were not evident. Despite this, she increasingly relied on AI for routine cognitive and interpersonal tasks including composing emails, interpreting social interactions, predicting the future and making decisions and felt increasingly uncomfortable completing such tasks independently. This case highlights the potential danger of generative AI tools in reassurance seeking behavior and how it might compound anxiety. These behaviors were observed across both professional and personal contexts, suggesting that AI use was not limited to task-specific assistance but reflected a generalized strategy for managing uncertainty and self-doubt. This behavioral pattern is characterized in this case by cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking processes. This led to the subjective belief of declining skill and compromised functional autonomy. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model was used to describe this pattern of behavior. While repeatedly turning to a family member or friend for validation and reassurance might lead to interpersonal fatigue, AI tools have no such limits and are infinitely accessible. This might ultimately worsen a patient’s ability to tolerate uncertainty. Given the ubiquity of AI tools and the prevalence of anxiety disorders, clinicians may need to be aware of the role AI tools potentially play in reassurance seeking and thus perpetuation of anxiety.
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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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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.
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