BACKGROUND
Depressive and anxiety symptoms (depression and anxiety hereafter), and suicidality are common among psychiatric patients. This study examined the network structure of depressive and anxiety symptoms and suicidality among psychiatric patients across Asia.
METHODS
Data were drawn from the Research on Asian Psychotropic Prescription Patterns for Antidepressants Phase 3 study (REAP-AD3), which included 2,455 psychiatric patients from 11 Asian countries and territories. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality (i.e., suicidal thoughts or acts) was assessed by a clinical interview. Expected Influence (EI) and Bridge EI were used as centrality indices in the symptom network to characterize the structure of the symptoms.
RESULTS
The point prevalence of suicidality was 30.1% (95% confidence interval [CI] = 28.2, 31.9) among the psychiatric patients. The network analysis identified PHQ2 ("Sad mood") as the most central symptom, followed by GAD2 ("Uncontrollable worry"). Additionally, PHQ8 ("Motor disturbances") and S ("Suicidality") were identified as bridge nodes linking depression and anxiety with suicidality. The flow network indicated that PHQ6 ("Guilt") and PHQ2 ("Sad mood") had the strongest positive associations with suicidality.
CONCLUSIONS
Suicidality was common among psychiatric patients across Asia. The central and bridge symptoms might represent potential clinical markers and generate hypotheses for longitudinal and interventional research on depression, anxiety, and suicidality in the future.
L. A, Yuan Feng, Qinge Zhang et al.· International Journal of Soc...· 0 citations
Objectives
This study aimed to understand how AI's perceived role interacts with emerging barriers and facilitators to identify factors influencing its adoption across clinical and educational professions in Singapore.
Methods
This study followed a qualitative approach guided by a medical-pedagogical theoretical framework. Semi-structured interviews were conducted between May and July 2025 and followed an interview guide based on the medical-pedagogical framework. Twenty-four people participated, including eight nurses, six psychiatrists, and 10 allied health professionals. All were clinicians and educators. Data were analysed using thematic analysis, with attention to emergent patterns across patient care and educational contexts.
Results
Participants recognised significant potential for AI in patient care and healthcare professions education, particularly for information access, retrieval, clinical documentation, AI-augmented training methods such as virtual patients and educational content creation. Barriers included fears of professional skill degradation, role confusion, lack of familiarity with capabilities and the need for personal evidence of benefit. Enablers encompassed integrated, context-specific training, clear governance frameworks, and peer networks facilitating experiential learning and responsible use. Participants emphasised maintaining human connection and reflective practice as essential to psychiatric care and education.
Conclusions
Effective AI adoption in psychiatric care and education can be facilitated by clearly delineating tasks, embedding digital literacy into training, communicating robust governance structures, and introducing peer-led communities of practice. Relevant stakeholders should be engaged to align AI deployment with real clinical workflows, optimising both patient care and educational outcomes.
D. Poremski, K. Wei, B. Ng et al.· International Journal of Med...· 0 citations
