Impaired insight in schizophrenia is consistently associated with poorer treatment adherence, more frequent hospitalization, and worse functional outcomes, yet the cognitive mechanisms that account for impaired insight remain incompletely specified. The neurocognitive model has long been the dominant explanatory framework but accounts for only a small fraction of the variance in clinical insight. This review synthesizes evidence on clinical and cognitive insight in relation to neurocognition and metacognition, identifies integrative metacognition as the principal explanatory bridge between basic cognitive resources and illness awareness, and develops the translational implications of this organization. Insight is treated as a multidimensional construct that includes clinician-rated awareness of illness and the metacognitive evaluation of one's own beliefs. Metacognition is shown to encompass distinct construct families with different bearings on insight, of which the integrative tradition shows the most theoretically central and empirically robust associations with clinical insight. Converging but largely indirect evidence is consistent with a multi-factor model in which integrative metacognition is hypothesized to partially mediate the relationship between neurocognition and clinical insight, while cognitive insight functions as an adjacent construct that overlaps with but does not exhaust metacognitive variance. The insight paradox is incorporated as a structural feature of the model, with internalized stigma possibly moderating whether heightened awareness translates into adaptive engagement or into depression and reduced quality of life. Interventions that target metacognitive processes, including Metacognitive Training and Metacognitive Reflection and Insight Therapy, produce measurable improvements in insight outcomes, whereas approaches without explicit metacognitive targets show limited and inconsistent effects. Longitudinal designs, mediation-focused intervention trials, and the integration of emerging candidate mechanisms such as mental time travel are proposed as priorities for consolidating the model. Literature was identified through searches of PubMed, PsycINFO, Embase, Web of Science, and Scopus up to May 2026, focusing on recent peer-reviewed work in adult schizophrenia-spectrum populations.

Perceived weight stigma is increasingly recognised as an important psychosocial stressor associated with adverse psychological well-being, impaired healthcare engagement, and poorer treatment outcomes. Adults diagnosed with schizophrenia may be especially vulnerable because of antipsychotic-related weight gain and the intersection of weight stigma and mental illness stigma. However, evidence regarding perceived weight stigma in this population remains scarce, particularly in low- and middle-income countries.

To characterise perceived weight stigma and identify factors associated with it among adults diagnosed with schizophrenia in China.

In this cross-sectional study, 301 adults diagnosed with schizophrenia completed standardized self-report measures assessing perceived weight stigma, weight bias internalisation, psychological distress, social support, coping styles, and sociodemographic characteristics. A generalized linear model with a negative binomial distribution and log-link function was used to identify factors associated with perceived weight stigma.

Overall, 54.2% of participants reported perceived weight stigma, operationalised as endorsement of at least one PWSQ item (PWSQ score ≥1). Lower monthly income, higher weight bias internalisation and greater psychological distress were associated with higher perceived weight stigma. BMI was not significantly associated with perceived weight stigma.

This study provides quantitative evidence on perceived weight stigma, an under-researched psychosocial issue among adults diagnosed with schizophrenia. In this Chinese clinical sample, perceived weight stigma was common and was associated with lower income, greater weight bias internalisation, and greater psychological distress. These findings highlight that perceived weight stigma is a clinically relevant psychosocial phenomenon. Shifting from symptom-focused psychiatric care to a broader social psychology determinants framework may be essential for supporting more comprehensive recovery-oriented interventions within this population.

Serious mental illness imposes a substantial burden. Pharmacologic treatments remain only partially effective and are associated with significant side effects, highlighting the need for novel treatment approaches. Ketogenic metabolic therapy (KMT) is emerging as an intervention for serious mental illness, with a growing evidence base and strong patient interest. A critical barrier to KMT implementation is the lack of health care professional (HCP) competence and readiness to discuss KMT with patients with serious mental illness. Existing training does not adequately address HCP competence in presenting KMT as a treatment option. This study describes the pilot-testing of "Live It, Launch It" (LI-LI), the first KMT training program designed to build self-reported competence to discuss KMT in psychiatric care for HCPs.

LI-LI combines a 4-hour asynchronous educational course on KMT with a 4-week experiential group KMT intervention during which participants adhered to a ketogenic diet. LI-LI aimed to improve self-reported competence to utilize KMT and perceived ability to engage in shared decision-making with patients about incorporating KMT into their treatment plan.

Forty HCPs participated in the education course: 10 psychiatrists, 10 psychiatric pharmacists, 8 psychologists/therapists, and 12 other HCPs (e.g., nurse practitioners, physician assistants). Twenty-five initiated the diet intervention. Baseline competence scores (visual analog scale 0-100) were low, M(SE) = 17.40 (2.68), increasing to M(SE) = 68.08 (2.47) after education (d = 2.88, p <0.0001), and to M(SE) = 86.62 (1.82) post-diet (d = 1.08), with a very large whole-study effect (d = 3.49, p < 0.0001). Shared decision-making scores at baseline were M(SE) = 53.64 (2.89), increasing to M(SE) = 83.35 (1.83) after the educational course (d = 1.74, p <0.0001), and to M(SE) = 91.23 (2.05) after the diet phase (d = 0.73, p = 0.009), with a very large whole-study effect (d = 2.53, p < 0.0001). Feedback indicated that lived experience with KMT was helpful for implementation-readiness.

The educational course on KMT was associated with increased self-reported competence and perceived shared decision-making in HCPs, and this was enhanced with combined education and lived experience. Implementation of this approach could accelerate KMT into clinical care. Results should be interpreted with caution as this was a small uncontrolled study with self-reported main outcomes.

https://clinicaltrials.gov/study/NCT07116226, identifier NCT07116226.

This narrative review examines the potential integration of narrative medicine into Rwanda's post-genocide healthcare system, where long-term psychological consequences continue to shape patient experiences and clinical practice. Despite significant improvements in health system performance since 1994, Rwanda's post-genocide context continues to shape emotionally demanding clinical encounters for patients and healthcare professionals. Narrative medicine refers to the clinical and educational capacity to attend to, interpret, and respond ethically to patients' illness stories. International literature suggests that narrative-based education may strengthen social and relational competencies, including empathy, perspective-taking, reflective capacity, and communication; however, these findings are context-dependent and require Rwanda-specific evaluation. In this review, the Chinese clinical narrative thinking framework serves as one possible analytic scaffold, not a model to be transferred uncritically. Its value is that it organizes narrative competence into four teachable clinical domains: narrative diagnostic thinking, narrative decision-making, narrative patient education, and narrative care or intervention. These domains are considered alongside foundational narrative medicine, illness narrative, reflective practice, and trauma-informed care frameworks. Although Rwanda has strong traditions of storytelling, testimony, and collective healing, these community and public practices are not direct equivalents of clinical narrative medicine. Narrative medicine has not yet been systematically integrated into Rwandan medical education or routine clinical practice, and any future integration would require trauma-sensitive safeguards, careful professional boundaries, and empirical evaluation. This review integrates deliberately selected global and context-specific literature to examine the theoretical underpinnings of narrative medicine, its significance to Rwanda's sociocultural storytelling and collective healing, and the gaps in medical education. While Rwanda-specific empirical evidence remains limited, the convergence of ongoing mental health needs, national workforce expansion, and strong narrative traditions presents a timely opportunity to develop trauma-responsive healthcare models relevant to post-conflict societies globally.

The rapid integration of digital technologies and generative artificial intelligence (AI) into higher education has reshaped first-year students' learning experiences and self-reported psychological adjustment. Using a cross-sectional self-report design, this study examined the statistical associations among AI-enabled course development, AI proficiency, academic adjustment, and a composite indicator of self-reported psychological adjustment.

A cross-sectional self-report survey was conducted among 551 first-year university students from a single institution. Descriptive statistics, Pearson correlations, multiple and hierarchical regressions, Bootstrap mediation analysis (PROCESS), moderated mediation analysis, and structural equation modeling (AMOS) were employed to examine the hypothesized associations. Causal inference is not warranted given the cross-sectional and self-report nature of the data.

Students reported high satisfaction with AI-enabled course development and strong acceptance of AI learning tools, with over 80% recognizing AI's learning efficiency and actively using AI for study. However, self-reported indicators of psychological strain were endorsed by more than two-thirds of the sample; these items are descriptive self-report indicators and do not constitute clinical diagnoses. Regression analyses showed that course development satisfaction and AI proficiency were statistically and positively associated with emotional management, stress coping, and academic adjustment. Hierarchical regression demonstrated that these variables substantially improved model explanatory power, with the highest explained variance observed for academic adjustment (R ² = 0.240). Mediation analysis indicated that AI proficiency partially mediated the association between course development satisfaction and self-reported psychological adjustment (indirect effect = 0.124, 95% CI [0.076, 0.181]). Moderation analysis revealed that AI-related usage pressure attenuated the positive association between AI proficiency and self-reported psychological adjustment (moderation index = -0.051, 95% CI [-0.095, -0.017]). Structural equation modeling showed acceptable model fit (χ ²/df = 2.31, CFI = 0.956, TLI = 0.941, RMSEA = 0.049, SRMR = 0.041).

In this cross-sectional sample, AI-enabled course development was statistically and positively associated with first-year students' self-reported psychological adjustment, both directly and indirectly through self-reported AI proficiency. Excessive self-reported AI-related stress attenuated these positive associations. Because the design is cross-sectional and self-report-based, these findings should be interpreted as statistical associations rather than causal effects. Universities may consider strengthening AI literacy training, optimizing course design, managing technology-related workload, and providing psychological support to maximize the potential benefits of AI-integrated education; future longitudinal and multi-institution studies are needed to test these associations more rigorously.

Post COVID-19 condition (PCC), is characterized by a wide range of symptoms, including fatigue and cognitive problems. Symptoms may lead to disability in everyday life and reduced health-related quality of life (HRQoL). Evidence for effective rehabilitation strategies for this patient group remains limited.

To examine the effectiveness of tailored individual follow-up compared with a standard one-day group course, both based on cognitive behavioral approach, in patients with PCC 6 months after the intervention.

A single-center, parallel-group, superiority randomized controlled trial (1:1) was conducted among outpatients aged 18-65 years with PCC. Patients were randomized to either a rehabilitation program with individual follow-up over 6-8 weeks or a standard one-day multidisciplinary self-management group course. Both groups were introduced to graded activity based on principles from cognitive behavioral therapy (CBT) and were offered the app "My COVID rehabilitation". The primary outcome was the between-group difference in patient reported HRQoL (EQ-5D-5L-index score) at 6 months. Secondary self-reported outcomes included fatigue measured with the Chalder Fatigue Scale, EQ-VAS, and symptom registration. Neuropsychological function was assessed with objective neuropsychological tests.

60 participants were randomized, the mean age was 46 years (22-66 years), and 43 (72%) were female. Outcome assessments were completed for the primary outcome for 29 participants in each arm. No significant between-group difference was found in the primary outcome (score difference EQ-5D-5L-index =0.07; 95% CI [-0.18,.04]; p = .11). The adjusted linear model confirmed the absence of significant difference between groups. No persistent significant group differences were observed for any secondary outcomes. Both groups showed statistically and clinically significant sustained improvements in fatigue and EQ-VAS, as well as significant reductions in the number of symptoms. Within neuropsychological test, both groups demonstrated significant improvement in episodic memory (RAVLT), and the standard course group additionally showed significant improvement in attention (CPT).

Tailored individual follow-up was not more effective than a standard one-day group course in improving HRQoL in PCC, both based on principles of CBT. However, significant improvements in both groups were observed in several secondary outcomes, including some objective neuropsychological tests. Larger studies are needed to confirm these results.Clinical Trial Registration: https://clinicaltrials.gov/study/NCT06085911, identifier NCT06085911.

Suicide is a major public health concern, and physicians are at increased risk because of demanding working conditions and a high prevalence of mental health problems. This study aimed to investigate the sociodemographic characteristics, depression, anxiety, burnout, coping styles, and suicide probability among physicians in Türkiye and to develop and interpret an explainable machine learning model for predicting suicide probability alongside conventional statistical analyses.

In this cross-sectional study, 769 actively practicing physicians completed a sociodemographic questionnaire, the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Suicide Probability Scale (SPS), Maslach Burnout Inventory (MBI), and Ways of Coping Inventory (WCI). Significant predictors of SPS scores were identified using multiple linear regression. An Extreme Gradient Boosting (XGBoost) model was subsequently developed, and SHapley Additive exPlanations (SHAP) were applied to interpret model predictions and quantify the contribution of individual predictors.

Multiple linear regression identified BDI, BAI, MBI-depersonalization, MBI-reduced personal accomplishment, WCI-optimistic approach, WCI-helpless approach, and WCI-seeking social support as significant predictors of SPS scores. The XGBoost model demonstrated good predictive performance, comparable to that of linear regression; SHAP analysis showed a broadly concordant pattern of feature contributions to the model's predictions, with depressive symptoms emerging as by far the most influential contributor to suicide probability.

The concordance between conventional regression and explainable machine-learning findings indicates that the two analytical approaches converge on a similar pattern of associations between depression, anxiety, burnout, coping style, and suicide probability; in this study, XGBoost did not provide a substantial predictive advantage over conventional regression, and its principal contribution was complementary model interpretation through global and individual-level SHAP explanations. Given the single-sample, single-split design of the present analysis, these explainable artificial intelligence findings should be regarded as hypothesis-generating rather than as a validated basis for clinical risk prediction, pending external validation.

Slow participant recruitment is one of the predominant determinants of failure or delay across clinical research. Even when recruitment targets are met, study populations may be unrepresentative due to sampling biases introduced by recruitment pathways. However, the effectiveness and demographic consequences of recruitment strategies are frequently underreported, undermining the generalisability of clinical findings and contributing to research waste. This study provides a data-driven, quantitative evaluation of multimodal recruitment strategies in psychiatric research, leveraging insights from the DOCUMENT study to synthesise a methodological framework for effective and representative participant recruitment.

Between June 2022 and December 2024, the study utilised a multimodal strategy to recruit participants with major depressive disorder (MDD), schizophrenia (SZ), and healthy volunteers (HV) for a two-phase study to investigate cognitive deficits across groups. Recruitment strategies included NHS clinical services, electronic health records, research registries, primary care sites, online and social media advertising, printed material, institutional resources, and word of mouth. For each avenue, yield, proportion of diagnostic group, recruitment rate, eligibility fraction, labour and financial cost, and demographic skew were quantified.

Across avenues, 194 participants were recruited (66 HV, 77 MDD and 51 SZ), with high retention (86-97%). Recruitment efficacy varied substantially by diagnostic group, with online advertising and research registries successfully recruiting MDD and HV participants but failing to recruit eligible people with SZ. Instead, SZ participants were primarily enrolled from labour-intensive clinical avenues (94%). Online recruitment showed higher accrual, but lower eligibility fraction compared to clinical pathways, revealing systematic sampling differences. Despite avenue-specific sampling biases, the multimodal approach yielded close demographic alignment to the 2021 UK Census for London in the study population. Data-driven adaptations, such as protocol amendments to eligibility criteria and online self-report triaging, improved study feasibility.

No single recruitment avenue was identified as sufficient for both efficient and representative psychiatric recruitment. Instead, multimodal strategies were necessary to dilute avenue sampling biases. Synthesising 30 months of data, we introduce a 10-point framework for enhancing recruitment effectiveness, feasibility, and representativeness. While grounded in UK-based psychiatric research, these principles are likely transferable to broader clinical research contexts to reduce research waste.

Not applicable.

The need for communication is common to all nurses during patient interaction in different medical scenarios, such as palliative care and end of life care. It is important to note that many nurses and nursing students perceive their level of training as inadequate to deal with difficult conversations. The two tools - virtual reality and artificial intelligence - provide practical help in achieving this.

To synthesize empirical evidence on artificial intelligence (AI)- and virtual reality (VR)-based communication skills training among nurses and nursing students and examine its implications for palliative care education.

Systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement.

Original studies written in English and published from database inception until March 2026 were identified. Studies that used AI- and/or VR-based communication training for nurses or nursing students using randomized controlled, quasi-experimental, mixed-methods, cross-sectional, or qualitative study designs were eligible for inclusion in this review. Thirteen studies were included in this review. Quality of the methodology was appraised using the Mixed Methods Appraisal Tool (MMAT).

Communication training through VR led to increased confidence in end-of-life communication, improved postmortem care knowledge, increased motivation and high acceptability. A 360 VR communication workshop was found to be feasible and scalable. Multiuser VR simulation reached the same communication outcome as live simulation, and VR-based communication training was more effective than video-based training for basic nurse-patient communication.

AI- and VR-based communication training represents an innovative tool for nursing education, specifically in the field of palliative and end-of-life care. However, further randomized controlled trials with objective performance measurements and long-term follow-ups are needed due to the heavy reliance on subjective self-reports in the existing literature.

Stress is an adaptive psychophysiological process that plays an important role in human survival and adjustment. Increasing evidence suggests that stress responses are shaped not only by environmental demands but also by cultural and self-related psychological processes. Self-construal, which refers to how individuals define themselves in relation to others, has been widely regarded as an important mechanism linking culture to cognition, emotion, motivation, and behavior. However, its role in stress-related processes has not been systematically synthesized. The current narrative review integrates evidence from cultural psychology, stress research, neuroscience and PTSD literature to assess the impact of self-construal on stress perception, emotional regulation, coping, stress recovery and vulnerability to stress-related disorders. Existing evidence suggests that interdependent self-construal (InterSC) is associated with increased sensitivity to social-evaluative settings, more acute stress reactions, and higher chronic stress levels. Concurrently, InterSC may also assist a faster recovery from stress and better sensitivity to social support in specific cultural circumstances. Evidence on independent self-construal (IndeSC) is relatively scarce, but available findings suggest that autonomy, personal objectives, and self-consistency may serve as relevant stress-related themes among individuals with high IndeSC. Thus, self-construal provides a valuable framework for understanding the ways that culturally molded self-processes influence stress responses, adaptability, and mental health outcomes, and may be useful for developing more culturally sensitive approaches to stress-related therapies.