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PubMed2026

Effectiveness of artificial intelligence in nursing simulation education: A systematic review, meta-analysis and bibliometric visualization analysis.

OBJECTIVES: To synthesize the roles and core functions of AI in nursing simulation education for nursing students via systematic review, quantitatively evaluate its effects on students' knowledge and skill outcomes through meta-analysis, and map the research landscape and development trends of this field through bibliometric visualization analysis. DESIGN: Systematic review, meta-analysis and bibliometric visualization analysis. DATA SOURCES: Eight electronic databases: PubMed, Web of Science, MEDLINE, ERIC, Academic Search Complete, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Chinese Science and Technology Journal Database (VIP) were employed to search studies from the time of construction to 16 December 2025. REVIEW METHODS: Studies meeting the inclusion criteria were screened. The revised Cochrane Risk of Bias tool (ROB 2) and Joanna Briggs Institute (JBI) critical appraisal checklists were used for quality assessment. Meta-analysis was performed with Review Manager 5.4, and bibliometric visualization analysis was conducted using VOSviewer 1.6.20 and Bibliometrix (based on R4.4.3). RESULTS: A total of 61 studies were included. AI primarily played two roles in nursing simulation education: peer-type new subject (n = 24) and direct mediator (n = 22). Meta-analysis showed that AI interventions significantly improved nursing students' knowledge (SMD = 1.49, 95% CI [0.55,2.43], p = 0.002) and skills (SMD = 0.66, 95% CI [0.02,1.31], p = 0.04). Bibliometric analysis identified that the United States of America and China were the two main contributing countries in this field, and the key motor themes included generative artificial intelligence, virtual patients, and geriatric care. CONCLUSIONS: AI exerts positive effects on nursing students' knowledge acquisition and skill enhancement in simulation education, with peer-type new subject and direct mediator as the dominant roles. Future research should focus on expanding AI applications in multi-specialty simulation scenarios, activating the data-driven value of machine learning, and strengthening international collaboration and standardization construction, so as to promote the sustainable development of AI-integrated nursing simulation education.

Humans↗

PubMed2026

Trends in demographic and health survey publications based on a bibliometric analysis.

BACKGROUND: The Demographic and Health Surveys (DHS) Program, launched in 1984, provides high-quality population health data that underpins a vast body of global health research. However, the scale and growth patterns of DHS-based publications remain underexplored, particularly as donor funding uncertainties threaten program sustainability. OBJECTIVE: We examine temporal trends in DHS-based research output from 1984 to 2025, quantifying growth patterns and publication delays to inform understanding of the program's global research expansion. METHODS: A systematic bibliometric review was conducted following PRISMA guidelines across PubMed, Scopus, Web of Science, Dimensions, Wiley, and CINAHL. Eligible peer-reviewed articles using DHS data between 1984 and 2025 were identified. Annual publication counts were analyzed, segmented regression identified growth inflection points, and timeliness was assessed by calculating lag between survey completion and publication. RESULTS: Over 10,000 DHS-based publications were identified. Annual output rose from isolated studies in the 1980s to several hundred annually by the 2010s. Segmentation analysis revealed two rapid growth phases: a 56-publications/year increase from 2004-2012, and a 71-publications/year increase from 2012 to 2024. Despite this growth, median lag from survey completion to publication remained approximately 5 years, with only a modest recent improvement (Kendall's τ =  -0.623, p < 0.001). CONCLUSION: DHS data have fueled exponential growth in global health research over four decades, confirming their vital role in evidence generation. However, persistent publication delays highlight the need to shorten the pathway from data collection to dissemination through strengthened research capacity in low- and middle-income countries. Sustained funding is essential to maintain this critical evidence source.

Bibliometrics↗

PubMed2026

Microplastic contamination in South Asian commercially important seafood: A comprehensive assessment of occurrence, source, and human health risk.

Seafood is a cornerstone of global food security and human nutrition, serving as the primary source of animal protein for more than one-fourth of the global population, with South Asia representing one of the world's fastest-growing seafood-consuming regions. However, escalating microplastic (MP) pollution in marine ecosystems poses an emerging threat to seafood safety and human health, yet a comprehensive regional assessment of MP contamination in South Asian seafood remains lacking. This study presents the first region-wide systematic synthesis of the literature on MP contamination in commercially important seafood across South Asia, integrating occurrence patterns, human exposure assessment, polymer-specific hazard evaluation, and bibliometric analysis to address this critical knowledge gap. The meta-analysis estimated an average microplastic exposure of 145 particles/person/day through seafood consumption in South Asia, with fish contributing the highest intake (121 particles/person/day). The detected polymers were classified into PHI hazard levels I-IV, with polyvinyl chloride (PVC), polyurethane (PU), and polyacrylamide (PAM) representing the highest hazard categories. The mean pollution load index (PLI) was 7.71 (Category I), with crustaceans exhibiting the highest contamination (PLI = 10.07). Polypropylene was the predominant polymer, whereas fragments and blue particles were the most frequently reported microplastic characteristics. These findings provide the first regional baseline for assessing microplastic contamination, polymer-associated hazards, and human exposure through seafood consumption in South Asia, underscoring the need for standardized monitoring and targeted mitigation strategies to safeguard seafood safety and public health.

Animals↗

PubMed2026

Toward real-time quantification of driving risks: a systematic review and research agenda of risk field theory.

In complex traffic systems, driving risk often evolves in a continuous and progressive manner prior to crash occurrence. How to effectively represent and analyze such latent risk states remains a central challenge in traffic safety research. In recent years, risk field-based approaches have introduced spatial and spatiotemporal continuous modeling paradigms, providing new perspectives for characterizing the distribution of traffic risk and its dynamic evolution. Motivated by the rapid growth of this research area and the lack of a systematic synthesis, this paper presents a comprehensive review of studies applying risk field theory to driving safety and traffic risk analysis. Following the PRISMA guidelines, relevant literature was collected through multi-database searches and analyzed using a combination of bibliometric analysis and qualitative review. The review systematically summarizes the theoretical foundations, modeling elements, data sources, analytical methods, and application domains of risk field-related research. Particular attention is given to studies that conceptualize traffic risk as a continuous field, complemented by a broader review of traffic risk factor literature to identify key elements and analytical dimensions involved in risk field modeling. On this basis, the paper synthesizes research progress in major application areas, including traffic safety state representation, driving behavior analysis, traffic conflict assessment, and autonomous driving and human-machine cooperative systems. Differences and commonalities among existing studies are compared in terms of modeling strategies, data support, and application scenarios. Through this systematic review, the paper clarifies the main research themes and methodological trends of risk field-based studies, providing a structured framework for understanding the evolution and application of this approach and offering methodological insights for risk perception modeling and safety-oriented decision support in intelligent transportation systems (ITS).

Humans↗