Despite the growing focus on cyberbullying victimization across scientific, clinical, and community spheres, an agreement on the most effective evaluation methodologies for evaluating its effects remains unclear. This research sought to examine the reliability, construct validity and measurement invariance of the Cyberbullying Test (CT) across age, sex, and self-reported behavioral and emotional problems, measured with the Strengths and Difficulties Questionnaire (SDQ). To accomplish this objective, we recruited 767 Romanian adolescents (aged: 11-18; M = 13.4 years). All analyses were performed using R with packages such as "lavaan" version 0.6.19. Confirmatory factor analysis (CFA) confirmed the three-factor structure of the measure (RMSEA = .001, SRMR = .025, and CFI = 1.00), while multigroup CFA supported measurement invariance across age, sex, and self-reported behavioral and emotional problems. This supports the comparison of means across sex, age, and the presence of self-reported behavioral and emotional problems. The main differences between the latent means were: (1) Females tend to engage less as cyberaggressors compared to boys; (2) Adolescents exhibit a higher frequency of involvement in cyberbullying as cybervictims or as cyberwitnesses compared to preadolescents and less as cyberaggressors, and (3) Those included in the group with high level of self-reported behavioral and emotional problems (compared to those with low levels of problems) are frequently involved in cyberbullying in all the three roles, with associated large effect sizes. Regarding reliability, Cronbach's alpha was .95 or higher for the three subscales. The present study establishes the CT as reliable and valid, advocating for its value in measuring cyberbullying implications among young people. Theoretical implications, evaluation considerations, and prospective directions are examined.

Broad memory challenges arise for people with clinically diagnosed mental health disorders, in part due to changes in hippocampal structure and/or function. However, individuals with mental health symptoms often show memory impairments, even when symptoms do not reach the level of a formal clinical diagnosis. Building on this, the present study examined how mental health symptoms influence performance on two relational memory tasks known to be critically dependent on hippocampal function. Participants from across the adult lifespan completed a transverse patterning task for which they learned directional relations among abstract objects. In a separate experiment, participants completed a transitivity task in which they learned relations between pairs of objects and subsequently made inferences across those pairs. All participants completed the Depression, Anxiety and Stress Scale (DASS-42). Results showed that individuals with greater symptoms of anxiety performed less accurately on both relational tasks, although this association was only marginal in the transitivity task. Greater symptoms of depression were associated with accuracy declines on both tasks, but only when greater stress levels accompanied depressive symptoms. In the transitivity task, additional interactions emerged between anxiety and both stress and depression, indicating that performance declines were exacerbated by co-occurring symptoms. Generally, older participants performed less accurately; however, in transitivity, these effects emerged primarily in those with accompanying stress and depression. These findings demonstrate that even subclinical levels of depression, anxiety, and stress are associated with memory impairments, suggesting that mental health may play a role in cognitive and brain health across the lifespan.

Faced with personal and study-related stressors, students in higher education experience elevated levels of depressive and anxiety symptoms while often showing low help-seeking behavior. Although interventions have shown promise in enhancing student well-being, their potential to address stigma, which is closely linked to help-seeking, remains largely unexplored. The present study evaluates The Inquiring Mind (TIM), a Canadian anti-stigma program for post-secondary education, adapted for the German university context, assessing its potential to reduce stigma, increase mental health literacy and encourage continuum beliefs, the understanding of mental health and illness as existing on a spectrum rather than as fixed categories.

A sequential explanatory mixed-methods design was applied, using a quasi-experimental online survey (pre-, post- and follow-up assessment), and a subsequent focus group with program participants. A total of 58 students participated. The mixed-methods approach was selected to gain a better understanding of the program's impact and the effectiveness of specific components.

Findings revealed significant reductions in stigma in the intervention group at post-test and a significant increase in continuum beliefs at follow-up. While quantitative results showed no improvements in mental health literacy, qualitative analyses revealed greater awareness of mental health issues and self-stigmatizing processes, as well as improvements in the normality aspect of continuum beliefs.

Findings support the applicability of TIM in a new cultural context, contributing to research on the emerging concept of continuum beliefs and to the understanding of how interventions can influence post-secondary students' stigma. This systematic evaluation of an early, campus-based prevention program offers relevant insights for developing prevention strategies addressing students' post-pandemic mental health needs across diverse cultural and geographic settings.

The Singapore National Precision Medicine (NPM) program is a three-phase whole-of-nation effort designed to develop scalable, evidence-based solutions for precision health tailored to Asia's diverse populations. Here we present NPM phase II (2020-2025), highlighting how large-scale precision medicine initiatives can drive new research insights, enable healthcare innovations and create economic value. Key achievements include the PRECISE-SG100K population dataset, which reflects Singapore's unique multi-ancestry Asian population; the successful translation of research findings into mainstream national healthcare using familial hypercholesterolemia as a use case; and the establishment of strategic public-private partnerships with diverse industry sectors. We also outline phase III (2025-2031), which aims to establish whole-genome sequencing as a foundational element of a patient's lifelong healthcare record for 10% of the population. NPM provides a model for how smaller countries, despite limited populations and finite resources, can contribute meaningfully to global scientific movements while preserving strategic independence and addressing national health challenges.

The growing integration of digital technologies into healthcare requires understanding the impact of different consultation modalities on patient stress, memory and perceived credibility. This study compared different consultation modalities (human physician, in person or via video call; AI-style physician, as a chatbot or avatar) in standardized simulated medical consultations involving the delivery of bad news. For the AI-styled physician consultations, a Wizard of Oz design was used, in which participants were told they interacted with an AI physician while a human controlled the interaction. 163 healthy participants experienced either a human or an AI physician. Stress was measured through ratings and salivary cortisol, alongside memory retrieval and situation credibility. Human-based consultations led to higher stress levels than AI-styled formats. Greater perceived credibility was associated with stronger stress responses. Memory retrieval was lowest in the AI-styled chatbot condition. These findings show that different consultation modalities are associated with varying levels of stress and memory in medical settings. AI-styled physician interactions may reduce stress in routine medical communication, but their use should be carefully considered for critical medical communication.

To map research on nursing workforce preparedness and resilience in health emergencies from 2000 to 2025 and propose an integrated framework.

Bibliometric review with science mapping and thematic evolution.

Scopus was searched for journal articles and reviews on nursing workforce preparedness and resilience in health emergencies published from 2000 to 2025. Records were screened using year, language, document type, duplicate title, and metadata-based scope criteria. Co-citation and co-word analyses were conducted to map intellectual foundations and conceptual structures. Time-sliced analysis examined thematic evolution across 2000-2009, 2010-2019, and 2020-2025, while emerging hotspots were identified using keyword growth and recency indicators.

The final retained corpus included 7570 documents, composed of 6950 articles and 620 reviews. Publication output showed a marked post-2020 surge. Co-citation analysis highlighted intellectual foundations related to disaster nursing competence, psychological resilience and mental health, leadership, staffing, safety, and workforce systems. Co-word analysis indicated major domains of clinical competence, emergency and acute-care delivery, pandemic-related psychological burden and resilience, workforce organization and emergency service capacity, and nursing education for disaster preparedness. Emerging hotspots included burnout, moral distress, resilience, telemedicine, mental health, and qualitative inquiry.

Nursing workforce preparedness research has evolved into a multidimensional, system-oriented agenda that integrates competence, resilience, and structural readiness.

Nurse managers should treat emergency preparedness as a strategic workforce-management responsibility, not only as a clinical or disaster-planning task. The findings support competency-based preparedness training, workforce policies for surge staffing and role deployment, organizational capacity building, mental health and resilience supports, leadership preparation, safety infrastructure, and data-informed preparedness planning to sustain nursing performance during future health emergencies.

Major depressive disorder (MDD) is a serious mental health disorder that is understood to affect an individual's speech signals, including observed variations in acoustic, prosodic, and laryngeal characteristics of a person's speech. There is a need for noninvasive, objective, and automated diagnostic methods for the diagnosis of depression. In this study, after applying the Empirical Mode Decomposition (EMD) to speech signals, it is proposed to classify the extracted features using a hybrid deep learning model (CNN-BiLSTM) for the early diagnosis of MDD. Nonstationary speech signals were decomposed into 7 intrinsic mode functions (IMF) using the EMD method. A total of 325 acoustic features (MFCC, LPCC, formant frequencies, jitter, shimmer, spectral entropy, glottal parameters, etc) were extracted from both the raw speech signal and the 7 IMF segments and classified using the CNN-BiLSTM model. Performance of the proposed method was compared with the performances of five different machine learning models for the MODMA dataset: Fine Tree, Linear SVM, Ensemble Boosted Trees, Kernel Logistic Regression, and Narrow Neural Network. The results show that EMD-based adaptive features significantly improve classification performance compared with features extracted without EMD. The study demonstrated better performance than MFCC or spectrogram-based studies in the literature, with 94.7% accuracy and an average F1-score value of 0.95. The results of 20% hold-out validation and 5-fold cross-validation demonstrate that the proposed method offers high accuracy, stability, interpretability, and generalizability. Additionally, a MATLAB-based clinical decision support interface supporting the CNN-BiLSTM model for MDD diagnosis from speech signals has been developed.

This article explores the mental health concerns of transgender and gender diverse (TGD) individuals within a mutual aid and harm reduction framework. Despite historical strides toward equity and care in the public health sector, TGD people face structural discrimination, polyvictimization, and worsening political hostility that heighten mental health disparities. The authors critique medical and nursing practices shaped by carceral logic and call for new approaches rooted in equity and care. Clinical recommendations emphasize affirming, trauma-informed, and community-based care, while highlighting harm reduction and mutual aid as essential, evidence-based strategies for collective healing, resilience, and a move toward communal care.

Overutilization of the emergency department (ED) is costly and often reflects unmet needs and suboptimal care, raising concerns when patients rely on the ED persistently over many years. Long-term ED use trajectories for patients with substance-related disorders (SRDs) have never been investigated. This study aimed to identify ED use trajectories over a 10-year period and examine associations with patients' sociodemographic and clinical characteristics, quality-of-care indicators, and subsequent adverse outcomes.

A cohort of 9642 patients with long-standing SRDs was examined using Québec (Canada) medical records (1996-2022). Group-Based Trajectory Modeling identified 10-year ED use trajectories. Multinomial logistic regression assessed associations between trajectories and covariates, while Cox models examined links to subsequent adverse outcomes (suicidal behavior, hospitalization, death).

Four ED use trajectories were identified: Trajectory 1 ("Low ED users," 50% of cohort), Trajectory 2 ("Increasing ED users," 20%), Trajectory 3 ("Sinusoidal ED users," 20%), and Trajectory 4 ("Very frequent ED users," 10%). Half of the cohort showed frequent ED use (≥3 visits/year), including 10% who were very frequent users (≥8 visits/year), underscoring substantial unmet needs. ED use frequency was strongly linked to social and health conditions-most favorable in Trajectory 1, who were mostly men, and markedly worse in Trajectories 4, 2, and 3. Adverse outcomes were also strongly associated with poorer social and health conditions and lower treatment motivation, even though Trajectories 4, 2, and 3 received more healthcare services.

Findings indicate that the healthcare system is not adequately structured to meet the needs of patients with severe multimorbidity involving SRDs, mental disorders, chronic physical illnesses, and social instability, contributing to care quality insufficiently meeting the complex needs of Trajectories 2-4. Tailored interventions may be warranted, including Integrated Treatment for Dual Disorders and Assertive Community Treatment for Trajectories 2 and 4, and Intensive Case Management, Recovery Management Checkups, or mobile health technologies for Trajectory 3. Across Trajectories 2-4, enhanced motivational interventions, social support, reduced stigma, optimized medication management, peer-helper programs, and stronger ED-outpatient collaboration remain essential. Improving services for patients with frequent ED use-particularly those in Trajectory 4 with recurrent very frequent use-should be prioritized to reduce costs, optimize service use, and support recovery.

Chronic psychological stress and poor cardiometabolic health are major public health concerns that can accelerate cellular ageing, yet their combined impact on shorter telomere length (TL) - a biomarker of cellular ageing - remains poorly understood in occupational settings. This study examined their associations and potential interactions among university staff in Australia.

TL was measured from blood leukocytes. Cardiometabolic indicators included adiposity, blood pressure (BP), lipid profile, fasting plasma glucose (FPG), insulin resistance, C-reactive protein, and cortisol. Psychological stress, burnout, well-being, and health-related quality of life (HRQoL) were assessed using validated questionnaires, and wearable devices captured sleep and nocturnal autonomic nervous system activity. Correlation, multiple linear regression, and moderator analyses were used to analyse the data.

Among 126 participants [65% female, mean age 48.84 (± 9.43) years], the average TL was 7.06 ± 1.00 kilobase. While visceral fat, systolic BP, and FPG were associated with TL, these associations were attenuated in age-adjusted models. Psychological variables were not directly associate with the TL; however, burnout (B = -0.26 [95% CI: -0.51, -0.01], p = .042), sleep-related impairment (B = -0.02 [95% CI: -0.04, 0.0004], p = .046), and HRQoL-mental component (B = 0.02 [95% CI: 0.001, 0.04], p = .034) significantly moderated the association between FPG and TL.

Age was the primary correlate of TL. Burnout, sleep-related impairment, and mental quality of life may moderate the association between FPG and TL. These exploratory findings suggest that addressing both psychological and cardiometabolic health may support TL in working adults.

ACTRN12625000134426.