Characterizing nursing home care team communication via text messaging: A social network analysis.

A robust food production system serves as the cornerstone for ensuring food security. Integrating the conceptual connotations of food production resilience, this study constructs a multi-dimensional evaluation index system encompassing resistance, recovery, and adaptive capacity. Utilizing panel data from Xinjiang spanning 2010 to 2022, the research employs an integrated methodology-including the entropy weight method, a modified gravity model, Social Network Analysis (SNA), and the XGBoost-SHAP model-to systematically analyze the resilience levels, structural network characteristics, and underlying driving mechanisms of food production in the region.The findings indicate that: From 2010 to 2022, the resilience level of food production in Xinjiang exhibited a continuous growth trend, characterized by spatial heterogeneity with a relatively narrow gap. During the study period, the network connectivity of food production resilience in Xinjiang became increasingly tight; however, it was characterized by a low network density along with a topology of high clustering and a short average path. Asymmetric features were observed between input and output regions, accompanied by a decrease in the number of spillovers across blocks. Per capita cultivated land area (X5), traffic accessibility (X15), agricultural technological progress (X12), and average annual temperature (X1) ranked as the top four influential factors, among which the interaction effect between per capita cultivated land area (X5) and traffic accessibility (X15) was the most significant. These research insights can provide valuable references for safeguarding national food security.

Objectives. To evaluate a place-based collective impact strategy on aligned multisector resources and perceptions of local youth wellness needs. Methods. Leaders and community members from Pierce County, Washington State, partnered with a university to develop a place-based youth wellness zone (YWZ). Two surveys, administered 9 months apart in 2023 and 2024, assessed social relationships within the network, ideas about youth needs, and willingness to invest in a pooled fund. Bimodal social network analyses evaluated changes in idea alignment over time. Results. Analyses showed increases in social network resource sharing within social clusters (C = 0.33 to 0.45). Ideas alignment strengthened over time concurrent with YWZ planning activities. No changes in overall network growth were observed. Motivation to contribute to a pooled fund was mixed, which was attributed to the early stage of implementation. Conclusions. Initiatives such as YWZs may influence the alignment of goals and resource sharing within communities. Social network analysis is a promising process and evaluation tool for collective impact efforts. Public Health Implications. Measuring degree of social connectedness and goals alignment may support successful implementation of place-based initiatives. (Am J Public Health. 2026;116(S3): S162-S170. https://doi.org/10.2105/AJPH.2025.308397).

A novel multi-criteria evaluation method for rumor-refutation effectiveness of rumor-refutation accounts on social media platforms integrating social network analysis

Mapping the triadic interaction network in AI-enhanced L2 classrooms: A social network analysis of learning outcomes and the moderating role of AI literacy

Role expansion of radiological technologists in intraoperative fluoroscopy in Japan: A simulation-based social network analysis of interprofessional collaborative structure.

ABSTRACT This study examines the structure and internal dynamics of the trans-Tasman (Australia and New Zealand) extreme right between 2010 and 2022 using Social Network Analysis (SNA). Drawing on two unique datasets, it maps how the network evolved through interpersonal and organisational connections, and in response to both internal dynamics and external events, across three key timeframes. It identifies the actors occupying central (vanguard) positions and their capacity to shape ideological flow across the network. The analysis reveals a decentralised, but connected, movement characterised by persistent ideological fragmentation, factional competition, and shifting patterns of influence. Positioning Brenton Tarrant within this network, the study finds that although he occupied a peripheral but connected role with limited capacity to influence broader movement strategy prior to 2019, he was power-adjacent and possessed knowledge of the mechanisms of ideological dissemination and instruction within the network. However, relationship breakdown and factional infighting in the period 2017–2018 reduced Tarrant’s ability to connect to the broader network and disseminate ideas through interpersonal channels. This study suggests that when pathways to influence within fragmented extremist networks are restricted, violence may emerge as an alternative mechanism for asserting influence.

Palliative care has been identified as one of the most inequitable areas of healthcare. In Switzerland, as globally, disparities in end-of-life care (EOLC) exist along socio-demographic lines, shaping the access to and quality of care received across services by patients and their caregivers. Research has linked these disparities to binary gender differences and other aspects of a person's social position. The GiveCare project aims to provide a systemic understanding of how aspects of gender and diversity intersect to shape the provision of EOLC in Switzerland and to translate the generated knowledge into practice, policy, education and training. GiveCare employs a sequential mixed-methods design, combining a feminist intersectional approach with a systems thinking lens. It consists of four work packages (WPs): (1) A national survey will assess the perceived awareness of gender and diversity among palliative care professionals. (2) A focused ethnography will provide in-depth insights into the care journeys of patients and their significant others in two specialised inpatient palliative care units. (3) A social network analysis and discrete event modelling will unpack the complexity of such care journeys across inpatient and outpatient settings in the Canton of Zurich. 4. Throughout the process, an integrated knowledge translation approach will help to generate actionable evidence, co-created with patients, caregivers, professionals and policymakers, to enhance inclusivity and equity in EOLC practice and policy. The Ethics Committee of the Canton of Zurich, Switzerland, granted ethical approval for WP2-4 (Req-2025-02241) and issued a waiver for WP1 (Req-2025-00211). The research team will conduct the research in accordance with the Swiss Federal Act on Data Protection and the rules and regulations of the Swiss Federal Data Protection and Information Commissioner. The findings will be disseminated through peer-reviewed publications and conference presentations as well as via the community of practice established through the integrated knowledge translation process.

ABSTRACT Knowledge-based urban development (KBUD) addresses urban sustainability in the knowledge era, focusing on fostering trust and stakeholder participation to create knowledge cities. This study critically evaluates the cluster approach, which assumes geographic proximity of knowledge-intensive activities enhances collaboration and knowledge exchange, using Isfahan (Iran) as a case study. Employing social network analysis and the QAP index, findings reveal that increased stakeholder communication does not necessarily improve trust or collaboration, challenging assumptions about specialised spaces like science parks. Instead, the study underscores the value of informal and public urban spaces for knowledge creation, advocating a broader view of urban knowledge.

The rapid adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) has transformed traditional workforce management by enabling intelligent recruitment, employee performance evaluation, talent analytics, workforce planning, and organizational decision-making. Simultaneously, the growing volume of research publications in AI-driven HRM has created complex collaboration networks among researchers, institutions, and countries. Social Network Analysis (SNA) provides an effective methodology for exploring these collaborative relationships by identifying influential authors, research communities, institutional partnerships, and knowledge diffusion patterns. This paper presents a comprehensive Social Network Analysis framework for examining research collaboration in AI-driven Human Resource Management. The proposed framework integrates bibliometric analysis, network construction, graph analytics, and visualization techniques to investigate co-authorship, institutional collaboration, countrylevel partnerships, keyword co-occurrence, and citation relationships. Graph-based metrics including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, network density, and modularity are utilized to evaluate collaboration strength and identify influential research communities. Comparative analysis demonstrates that AI-driven HRM research has experienced significant global collaboration growth, interdisciplinary knowledge sharing, and increased institutional connectivity over recent years. The findings provide valuable insights into emerging research trends, collaborative structures, influential contributors, and future research opportunities within AI-enabled Human Resource Management. The proposed framework contributes to strategic research planning, policy formulation, and international scientific collaboration by providing a data-driven understanding of the evolving research ecosystem.

Dengue fever is a global public health issue of international concern. The southwestern border regions of China (Yunnan Province) are an important gateway for imported dengue fever cases in China. This study aims to investigate the importation patterns of imported dengue cases in Yunnan Province to support global dengue prevention efforts. This study examined imported dengue cases in Southwestern border regions of China, comparing actual and predicted cases via a counterfactual model under COVID-19 measures. Social network analysis was used to identify the importation pattern. From 2012 to 2023, 6,205 imported dengue cases were reported, making up 20.2%. The main sources were Southeast Asian countries: Myanmar (86.6%), Laos (5.8%), and Cambodia (4.9%). During the COVID-19 pandemic, imported cases dropped dramatically when borders closed, with predicted case counts being approximately 16.2-fold, 315.3-fold, and 12.3-fold higher than observed counts from 2020 to 2022, respectively, reflecting the sharp reduction in cross-border mobility during this period. In 2023, observed cases exceeded predictions by 45.0%. The cumulative social network identified three transmission communities centered around Kunming and Xishuangbanna in China, and Myanmar. The community centered in Myanmar had a bridging closeness centrality of 3.67, mainly connecting to Chinese border cities like Dehong (3.7) and Lincang (3.7). The Xishuangbanna community had a centrality of 3.4, primarily linking to Laos (3.4). The Kunming community had the highest centrality at 4.4, connecting mainly to non-border countries including Thailand (4.0), Cambodia (4.3) and Malaysia (2.8). Imported dengue cases in China's southwest border regions exhibit a dual-center pattern: land-border cases cluster in cities adjacent to Myanmar and Laos, while air-border cases concentrate in Kunming. Future efforts should enhance port monitoring and cross-border cooperation.

While emotions are socially shaped, research on academic emotions has neglected the potential influence of peer emotions. This study investigated whether students' boredom is influenced by their classroom friends' boredom and whether teacher-student relationship quality and cognitive ability moderate these influences. Participants were 3415 7th Grade students (Mage = 12.44(0.45); 49.69% females) from 27 Flemish schools. Longitudinal social network analyses demonstrated peer influence effects on academic boredom, controlling for selection and network effects. Teacher-student relationship quality and cognitive ability did not moderate this influence, but higher teacher-student relationship quality predicted less increases in boredom. Findings indicate that boredom can spread through classroom friendship networks, regardless of student ability, and that high-quality teacher-student relationships protect against increases in boredom.

Evaluating the play fighting of rats: A sociological perspective.

Six new kromycin-type macrolides, kromycins B-G (1-6), and with the known kromycin A (7), were isolated from Streptomyces narbonensis sp. CPCC 206470 guided by the Global Natural Products Social Molecular Networking (GNPS) analysis. Their structures were comprehensively established by extensive spectroscopic analyses, time-dependent density functional theory electronic circular dichroism (TDDFT-ECD) comparison, and single-crystal X-ray diffraction analysis. These compounds represent a structurally distinct subclass of 14-membered macrolides. Compound 7 exhibited substantial neuroprotective activity by inhibiting Aβ42 aggregation and ameliorating oxidative stress and neuroinflammation in N2a neuronal cells. It enhanced antioxidant enzyme activities (SOD and CAT) and suppressed nitric oxide production. Genomic analysis identified a pikromycin-related biosynthetic gene cluster in the producing strain. This study expands the chemical diversity of kromycin-type natural products and highlights their potential as multitarget therapeutic agents for neurodegenerative diseases.

Purpose This study aims to investigate the coevolution of supportive social ties and psychological distress within The Phoenix, a sober active community. It also aims to disentangle two concurrent processes: whether psychological distress influences the formation of social ties (social selection) or whether peer relationships influence individual distress levels (social influence) over time. Design/methodology/approach Longitudinal social network data were collected from members of The Phoenix in Denver and Wichita (n = 23) across two waves separated by one year. This study used the Simulation Investigation for Empirical Network Analysis (SIENA) framework to simultaneously model structural network dynamics and changes in psychological distress. Findings Network ties were characterized by reciprocity and clustering. A significant negative selection effect was identified (PE = −0.37, p < 0.05): members with lower psychological distress were more likely to be nominated as supportive partners, while those with higher distress were less likely to be selected. No evidence of peer influence on psychological distress was observed over the one-year period. Research limitations/implications Limitations include small network sizes and a two-wave design, which restricted model complexity. Future research should incorporate additional waves, larger networks and mixed-method approaches to capture nuanced changes in mental health and social integration. Practical implications Findings suggest that members with higher psychological distress are less likely to be selected as supportive ties, potentially isolating those most in need of connection. “Structured socialization” strategies, such as assigned mentorships or care circles, could be implemented to proactively integrate members with higher distress levels who might otherwise be filtered out by the community’s selection mechanisms. Social implications While recovery communities provide vital protection, they can also function as exclusionary structures. To ensure equitable access to recovery capital, communities must adopt intentional strategies that mitigate social stratification based on mental health status. Originality/value This study addresses a gap in recovery literature by using longitudinal network analysis to separate selection from influence. It provides empirical evidence that mental health status drives social connectivity in recovery settings, rather than connectedness simply improving mental health.

Dementia is one of the greatest global health challenges requiring multimodal efforts to address its impact on people and societies. Walking the Talk for Dementia (WTD) is a unique global initiative designed to challenge the stigma that surrounds dementia, foster intergenerational dialogue, build a community, and inspire a renewed sense of purpose among participants. A social network analysis (SNA) was conducted to provide greater detail and contextualization of the connections made during WTD and insights into the collaborations that arose from this immersive event which may influence dementia policy, care, and services. A robust, comprehensive, and minimally intrusive evaluation was created in 2024 to capture the results, outcomes, and impacts of WTD 2024. This mixed methods research examined the experiences of the WTD 2024 participants, 79 of whom consented to participate in this research. Pre- and post-surveys collected quantitative and qualitative data (94% and 87% response rates), and were augmented by 95 written, audio, or video reflections. To further investigate the social network of the larger WTD community, a follow-up survey was sent six months after WTD 2024 to the 2024 participants plus the additional 40 participants from WTD 2023. Quantitative survey data were analyzed with descriptive and inferential statistics; qualitative data were analyzed using content analysis to identify core aspects and common themes of the experience. A social network analysis (SNA) was used to visualize and quantify connections between participants of WTD. Results from the SNA analyses illustrate the development of multiple new connections and collaborations among WTD participants. The results also point to how connections and collaborations among participants that existed prior to WTD 2024 may have changed as a result of WTD. Connections appear to have increased post-WTD. A community developed among WTD participants, who are a multi-disciplinary, global group of professionals and people with lived experience of dementia. This analysis provides evidence of the benefits of social connections for individuals living with dementia and their care partners with other dementia advocates. These findings are important to future advancement of dementia awareness, advocacy, and policy change.

BackgroundEffective collaboration among intensive care unit (ICU) nurses is critical for patient safety, yet traditional analyses often overlook the relational dependencies inherent in teamwork. This study aimed to delineate task‐specific collaboration structures and identify pair‐level determinants using network‐aware methodologies.MethodsA cross‐sectional study was conducted in a tertiary ICU involving 96 registered nurses during day shifts from January 1, 2025, to February 1, 2025. Collaboration was defined as coparticipation in clinical events and categorized into four task types: emergent response, procedural care, patient flow, and safety double‐checks.ResultsEvent‐based networks revealed distinct topologies: Emergent care showed compact clustering, while procedural care displayed hub‐and‐spoke configurations centered on specialists. MRQAP analysis (R2 = 0.27) identified work assignment proximity as the strongest predictor of collaboration (β = 0.314, p < 0.001). Psychological safety similarity (β = 0.176, p = 0.004) and competency complementarity (β = 0.142, p = 0.021) were also significant, while tenure similarity showed a borderline association.ConclusionsICU nurse collaboration is multidimensional and task‐dependent. Managerial strategies should prioritize optimizing roster designs for spatial proximity, fostering a psychologically safe climate, and strategically developing complementary competencies to enhance team resilience and care quality.

We aimed to examine advice interactions among family physicians using social network analysis (SNA) by categorizing advice interaction according to the five advice dimensions. Inter-individual interactions for information exchange is a powerful tool for the pursuit of solutions to issues. These interactions may involve advice-seeking. The whole network approach was adopted and face-to-face research was conducted with 139 family physicians. Data were analysed using social network software, UCINET and visualized using the NETDRAW software. To examine the multidimensional advice networks, the frequency, density, reciprocity (dyad) measures were used. The Quadratic Assignment Procedure was used in UCINET to measure the correlations between the dimensions of advice. The Girvan-Newman algorithm was used to examine clustering in the advice network. Density values in the advice dimensions were very low. This indicates that the network was sparse, with limited interactions among family physicians in terms of giving and receiving advice. The strength of the ties in the dimensions was realized through validation, solutions, problem reformulation, meta-information, and legitimization, respectively. The results showed that the relationships between the dimensions were moderately, positively and significantly correlated. The advice network exhibited high modularity. Family physicians tended to seek advice from colleagues at the family health centers where they worked. We presented a visual representation of advice networks in primary healthcare settings. Identifying multidimensional advice networks through social network analysis can provide insight into how information is disseminated among family physicians. Our findings could contribute to decision makers in developing solution-oriented processes.

Online health communities (OHCs) have emerged as critical platforms for patients with type 1 diabetes (T1D) to exchange informational and emotional support. However, how stakeholder roles and disease duration jointly shape support dynamics and influence formation remains underexplored. This study aimed to examine network-based social support mechanisms in a large T1D OHC, focusing on how stakeholder diversity and disease duration are associated with social support behaviors, subnetwork structures, and user influence. This retrospective observational study analyzed digital trace data from China's largest T1D online community (January 1-May 20, 2024), comprising 43,788 posts and 145,423 comments contributed by 1393 users. We manually annotated 2000 randomly sampled posts and fine-tuned a GPT-4o-mini (OpenAI) to classify support type (informational or emotional, and seeking or providing), yielding 20,384 support-related posts and 56,953 comments from 1224 users. We constructed weighted directed informational and emotional interaction networks and modeled predictors of a composite influence metric (Relative Centrality) using a gamma log-link generalized linear model (including demographics, identity, sentiment, disease duration, posting orientation, and cyclical activity time). Analyses were conducted in Python (version 3.11; Python Software Foundation). Statistical significance was set at P<.05. Support predominantly flowed from longer-duration members (≥ y) to those at earlier stages (≤5 y). Both subnetworks exhibited multicentered, star-like structures; the informational subnetwork had broader participation (density 0.031, diameter 7), while the emotional network was denser (density 0.039, diameter 6). In the influence model, peer supporters had substantially higher influence than patients (exp(β)=34.79, 95% CI 18.94-64.08; P<.001), professionals lower (exp(β)=0.41, 95% CI 0.17-0.99; P=.055), and women higher than men (exp(β)=1.65, 95% CI 1.23-2.23; P=.001). Positive sentiment was associated with higher influence (exp(β)=1.91, 95% CI 1.22-2.97; P=.005), and negative lower (exp(β)=0.54, 95% CI 0.37-0.79; P=.001). Influence followed an inverted U-shaped trajectory over disease duration, peaking at approximately the 116th month (95% CI 43.25-188.91). This study suggests that social support patterns and user influence in a T1D OHC vary by stakeholder role and disease duration. Users with shorter disease duration more often sought support, whereas longer-duration users more often provided support, and informational and emotional exchanges formed distinct interaction subnetworks. Peer supporters were the most influential users; influence was also associated with gender, sentiment, activity timing, and a nonlinear (inverted U-shaped) relationship with disease duration. These findings may inform peer-facilitated, stage-tailored community strategies, with professionals engaged in targeted, complementary roles. A patient-centered collaborative care approach integrating peer experience with multidisciplinary clinical input could be explored in future work.

Social media platforms are important spaces for social and sexual networking among men who have sex with men (MSM). However, limited research has examined structural changes in MSM digital social networks or the contribution of different user roles in maintaining network connectivity. This study aims to investigate structural changes and user dynamics in an online MSM network to inform tailored digital public health interventions. We constructed directed social networks among MSM in Zhuhai, China, using large-scale Blued platform data collected in 2021 and 2024. Users were represented as nodes, and follow relationships were represented as directed edges. Key network metrics, including degree distribution, reciprocity, assortativity, community fragmentation and attribute-based homophily, were analysed to identify weak links and structurally important users. The analysis included 9409 valid users in 2021 and 8890 in 2024. The network was significantly sparser and more fragmented, with edge density halving and 70.91% of detected communities containing only two users in 2024. Average degree dropped from 19.52 to 8.00, whereas degree assortativity decreased from -0.0580 to -0.1482, indicating increasing disassortative mixing. Despite reduced platform engagement, younger, versatile, and highly active boundary and common users remained important for sustaining inter-community connectivity. From 2021 to 2024, the MSM network on Blued became more fragmented, disassortative and heterogeneous. These changes may constrain health-message diffusion. Network-informed strategies targeting central and bridging users, while improving access for peripheral users, may enhance equitable information dissemination among MSM communities.