Social network effect on land transfer willingness of the rural elders: Evidence from China.

The elders in China's rural areas are facing challenges in maintaining agricultural production due to the outflow of rural laborers. The Transfer of land could alleviate the burden of land-based livelihoods for rural elders, but their decisions regarding land transfer are influenced by their social networks within the context of Chinese rural society. This study investigates how social networks impact the willingness of rural elders to transfer land. Using survey data from 782 rural elders in 32 villages across 11 provinces in China, this paper applies multilinear and binary logistic regression models. The results indicate that the willingness of rural elders to transfer land is affected by their social neteork: (1) Internal network scale, network heterogeneity, and frequency of external network relationships have a significantly positive influence on rural elders' willingness to transfer land, while frequency of internal network relationships has a significantly negative influence. (2) There are group differences in the above impacts, and these significant impacts occur only among male elderly individuals aged 60-69 years old or living in central and western regions. (3) Social networks primarily influence rural elders' willingness to transfer land through three mechanisms: information consultation, interpersonal trust, and material resource acquisition. A larger internal social network scale, higher heterogeneity within the network, and more frequent interactions with members of external networks lead to greater access to useful information, higher levels of trust in others, increased material resources availability, and an increased likelihood of transferring land. These findings can inform government policies aimed at improving practices related to land transfers and old age security for rural elders.

Deep Learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes.

The plurivocal university: Typologizing the diverse voices of a research university on social media.

Science communication has diversified in the wake of the digital transformation of communication and media ecosystems. Social media enable universities, but also academics and institutions affiliated with them, to expand their communication. This leads to increasing plurivocality of universities, yet the many different voices remain largely unexplored. This study develops a typology to conceptually distinguish eight voices by their representational role, hierarchical embeddedness, type, and affiliation. Based on a quantitative content and social network analysis of more than 600 Twitter accounts linked to a research university, it identifies six types of voices empirically. The study compares interactions among these voices, showing differences between central and decentral, as well as institutional and individual voices, and highlighting closer exchanges between voices within the same disciplinary communities. It also examines topics and tonality, revealing that decentral institutional voices engage most in science-related topics, and that only current and former students express critical views.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province.

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis.

Background: Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021. Objective: This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags #goASCO20 and #goASCO21 in the ASCO 2020 and 2021 virtual conferences. Methods: New hashtags (#goASCO20 and #goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (#goASCO20 for 2020 and #goASCO21 for 2021). A social network analysis was performed using the NodeXL software application. Results: The hashtags #goASCO20 and #goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found #goASCO20 to have 150 vertices and 2519 total edges and #goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the #goASCO21 hashtag received significantly more responses than tweets using #goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year. Conclusions: Introducing a gynecologic oncology specialty-specific hashtag (#goASCO20 and #goASCO21) that is related but different from the official conference hashtag (#ASCO20 and #ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis.

The impact of social network embeddedness, TPB, and danwei system on residents' participation intention in old neighborhood regeneration.

Residents' participation is crucial for sustainable old neighborhood regeneration. Residents' intentions to participate in the regeneration are low in China, resulting in unsustainable neighborhood development problems. Therefore, it is essential to investigate the drivers of residents' participation. Residents' behavioral mechanisms are deeply embedded in their social networks, especially in acquaintance social networks typical of old neighborhoods in China. By constructing a mediated moderation model based on the Theory of Planned Behavior (TPB) and danwei system and taking the old neighborhood regeneration in Xi'an as a case study, this paper investigates the paths and mechanisms of the social network embeddedness, TPB, and danwei system on residents' participation intention. The results show that social network embeddedness can improve residents' intention to participate in regeneration through both direct and indirect pathways, and its indirect effect is much larger than the direct effect. Attitude, subjective norm, and perceived behavioral control significantly and positively mediated the contribution of social network embeddedness on participation intention. However, the specific mediating effect of attitude is significantly smaller than the other two. The danwei system has a significant moderating effect in the first stage of the relationship between social network embeddedness and participation intention. These imply that the extrinsic social factor of social network embeddedness is internalized as a driving force for convergence and motivation of residents' participation intentions mainly through the normative pressure and exchange of experiences between acquaintances and that the danwei system reinforces this extrinsic-intrinsic facilitation process through the danwei's "physical presence." These findings provide revelatory countermeasures for exploring the factors that motivate residents to participate actively in neighborhood regeneration and encourage their involvement at the intervention level.

Are social supports always protective? A seven-city study on heavy drinking among sexual and gender minority young adults experiencing homelessness.

PURPOSE: Sexual and gender minority (SGM) young adults are disproportionately impacted by homelessness and heavy drinking (i.e., having five or more drinks of alcohol in a row within a couple of hours). Social support, in general, is protective in reducing individuals' risk of heavy drinking. However, whether and how support from different sources may have different implications on heavy drinking among SGM young adults experiencing homelessness (SGM-YAEH) remains unclear. Informed by the risk amplification and abatement model (RAAM), this study examined the associations between support sources and heavy drinking among SGM-YAEH. METHODS: A purposive sample of SGM-YAEH (N=425) recruited in homeless service agencies from seven major cities in the U.S. completed a self-administered computer-assisted anonymous survey. This survey covered heavy drinking behaviors and social network properties. Logistic regression models were conducted to identify social support sources associated with SGM-YAEH's heavy drinking. RESULTS: Over 40 % of SGM-YAEH were involved in heavy drinking in the past 30 days. Receiving support from street-based peers (OR=1.9; 95 % CI=1.1, 3.2) and home-based peers (OR=1.7; 95 % CI=1.0, 2.8) were each positively associated with SGMYAEH heavy drinking risks. CONCLUSION: This study was not able to identify the protective role social supports may play in reducing SGM-YAEH's heavy drinking. Furthermore, receiving support from network members was correlated with elevated heavy drinking risks among this population. As heavy drinking prevention programs develop interventions: they should use affirming and trauma approaches to promote protective social ties, as research points to its association in reducing alcohol use disparities among SGM-YAEH.

Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study.

BACKGROUND: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data. OBJECTIVE: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups. METHODS: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists' society's national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post. RESULTS: Of the 341 medicine shortages, 102 (29.9%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0%) and health care professionals (n=46, 45.1%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1%) was the most common category. CONCLUSIONS: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages.

The evolution of plant phenomics: global insights, trends, and collaborations (2000-2021).

Introduction: Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications. Methods: The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET. Results: Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges. Discussion: The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.

Developing a rural paediatrician workforce: Using social network analysis to examine influence on reasons to go rural.

AIM: This pilot study examines how rural and remote junior doctors' career decisions are influenced by collegial relationships within the discipline of general paediatrics. METHODS: Social network analysis (SNA) was undertaken by structured interviews with 10 paediatricians working in regional towns in Western Australia. UNICET software was used to determine the interactions between individual networks to look for overlap and common influencers. RESULTS: Ten rural paediatricians were interviewed. An individual was found to have key measures of centrality at the core of the entire social network of rural general paediatricians. This included a high degree of 'betweenness' (connections within social networks), and a high broker index (connections between separate areas of a network or between networks) demonstrated by that person combining three disconnected networks into a single coherent network. This central individual was a recently appointed consultant with links to senior paediatricians, peers and junior trainees, and may be instrumental in recruitment and retention in the rural paediatric workforce. CONCLUSION: Improving understanding of the impact of social networks, and decision-making processes that influence rural career choices, can inform innovative solutions to develop sustainable strategies for recruiting and retaining the rural paediatric workforce. Applying this model on a larger scale may provide more data to support evidence-based programmes that enable this within the Australian context.

Perceptions of Supportive Factors for Reducing Risk of Maternal Mortality Among Women With Substance Use Disorders in a Rural Setting.

OBJECTIVE: To describe perceptions of supportive factors for reducing the risk of maternal mortality among women with substance use disorders (SUDs) in a rural setting. DESIGN: Qualitative descriptive design. SETTING/LOCAL PROBLEM: Participants were recruited from a rural setting in a U.S. Midwest state where rates of maternal substance use and maternal mortality are high. PARTICIPANTS: Sixteen participants were recruited from a maternal residential substance use treatment center. INTERVENTION/MEASUREMENTS: Semistructured interviews were used during which participants described their perceptions of maternal mortality and their related experiences. We analyzed the transcribed interviews using a basic inductive content analysis to yield themes and subthemes. RESULTS: We identified three main themes: Social Networks, Respectful Perinatal Care, and Residential Substance Use Treatment. CONCLUSION: Our findings suggest that nurses and other health care providers should be knowledgeable of resources to increase the social networks of women with SUD, recognize and manage the biases and judgments they may hold against women with SUD, and advocate for and refer women with SUD to residential substance use treatment.

Persistent species relationships characterize migrating bird communities across stopover sites and seasons.

Global migrations of diverse animal species often converge along the same routes, bringing together seasonal assemblages of animals that may compete, prey on each other, and share information or pathogens. These interspecific interactions, when energetic demands are high and the time to complete journeys is short, may influence survival, migratory success, stopover ecology, and migratory routes. Numerous accounts suggest that interspecific co-migrations are globally distributed in aerial, aquatic, and terrestrial systems, although the study of migration to date has rarely investigated species interactions among migrating animals. Here, we test the hypothesis that migrating animals are communities engaged in networks of ecological interactions. We leverage over half a million records of 50 bird species from five bird banding sites collected over 8 to 23 y to test for species associations using social network analyses. We find strong support for persistent species relationships across sites and between spring and fall migration. These relationships may be ecologically meaningful: They are often stronger among phylogenetically related species with similar foraging behaviors and nonbreeding ranges even after accounting for the nonsocial contributions to associations, including overlap in migration timing and habitat use. While interspecific interactions could result in costly competition or beneficial information exchange, we find that relationships are largely positive, suggesting limited competitive exclusion at the scale of a banding station during migratory stopovers. Our findings support an understanding of animal migrations that consist of networked communities rather than random assemblages of independently migrating species, encouraging future studies of the nature and consequences of co-migrant interactions.

Protocol for a pilot cluster randomised controlled trial of PRoGRAM-A (preventing gambling-related harm in adolescents): a secondary school-based social network intervention.

BACKGROUND: In the UK, recent evidence of young people and gambling indicates a higher prevalence of gambling in comparison to other addictive behaviours. Engaging in gambling-related behaviour at a young age is associated with short and long-term consequences, including financial, emotional, academic, interpersonal, and physical and mental health detriments; otherwise known as gambling-related harms (GRH). Given the unique vulnerability of this younger group, early interventions aimed at delaying or preventing gambling are critical. PRoGRAM-A (Preventing Gambling-Related Harm in Adolescents) is a school-based, social network intervention to protect young people from future GRH, by delaying or preventing gambling experimentation. METHODS: Pilot cluster RCT with an embedded process evaluation and health economic scoping study. PARTICIPANTS: PRoGRAM-A will be delivered in four schools, with two control schools acting as a comparator. All are secondary schools in Scotland. Baseline surveys were conducted with students in S3 (ages 13-14). Follow-up surveys were conducted with the same cohort, six months post-baseline. INTERVENTION: PRoGRAM-A trainers will deliver a 2-day, out-of-school training workshop to Peer supporters. Peer supporters will be nominated by peers among their school year group (S3, age 13-14). Workshops will provide peer supporters with information on four gambling-related topics: (1) what is gambling? (2) gambling and gaming, (3) gambling marketing, (4) identifying harm and reducing risk. Peer supporters will disseminate the information (message diffusion) they have learned among their friends and family over a 10-week period. After the 2-day workshop, PRoGRAM-A trainers will conduct × 3 in-school follow-up sessions with peer supporters to offer support, encouragement, and advice to Peer Supporters as well as monitor and explore the extent of their message diffusion. PRIMARY OUTCOME: The primary outcome of the pilot cluster RCT (cRCT) will be whether progression to a phase III RCT is justified. DISCUSSION: This will be the first pilot cluster RCT (cRCT) of an intervention to prevent gambling-related harms among young people within the UK. If findings indicate feasibility and acceptability, funding will be sought for a phase III RCT of effectiveness. TRIAL REGISTRATION: Researchregistry8699. Registered 21st February 2023.

[Teenage pregnancy: when digital social networks support becoming a mother]. / Grossesse à l'adolescence : quand les réseaux sociaux numériques viennent supporter le devenir mère.

The onset of pregnancy during adolescence brings with it upheavals for the body, identity, family and society. This particular context can make some teenagers psychopathologically fragile, while others adapt perfectly. When these mothers-to-be receive support, their adjustment to pregnancy seems to be facilitated. At present, it seems easy for teenagers to maintain contacts and thus benefit from a form of support on digital social networks. Recently discussed in the literature, the latter could represent a source of social support in this type of pregnancy, but also of support for the identity process of becoming a mother.

Conceptualizing the social networks of children of parents with serious mental illness: a thematic analysis.

Aims: Social networks, defined as the set of active and significant ties surrounding an individual, influence the wellbeing of vulnerable children. The best evidenced mechanism through which this occurs is where networks act as a vehicle to access social support. Little is known about the content and function of social networks of children of parents with severe and enduring mental illness (COPMI). COPMI are a frequently under-identified vulnerable child population at risk of negative outcomes. This qualitative study investigates the structure, role and function of these children's networks. Methods: Researchers conducted 17 semi-structured egocentric social network interviews. Interviews incorporated personal network mapping as a data collection method. COPMI were recruited through third sector organizations and interviewed across three sites in England. Data was analyzed using an inductive thematic analysis. Results: Five network features were identified (i) parents as primary providers of support (i) limited networks and diminished connections over time (iii) substitutable ties (formal and informal) (iv) peer connections as source of both support and strain (v) coping strategies: self-censorship, avoidance and animals. Conclusion: Children of parents with severe and enduring mental illness networks are structurally typical of vulnerable children in that they are limited, rely on parents as primary ties but allow for some substitution of support ties. COPMI-specific features included peer relationships at times as source of strain and network level coping strategies used to manage wellbeing, including pets. This latter reflects previous findings in vulnerable adult populations so far unevidenced in children. Little evidence as to the mechanistic effect at work within networks was collected. However, COPMI were clearly shown to be engaged in active management and strategising in network navigation approaches, indicating the need to engage with children in this capacity, rather than approaching them as passive recipients of support. As such, effective network level interventions for this group are likely to prioritize access to beneficial substitute ties when support is limited. Additionally, interventions that promote network navigation skills and help foster productive coping strategies can capitalize on the child's active management role within their network.

[Loneliness in unemployed individuals diagnosed with mental illness]. / Einsamkeit bei Arbeitslosen mit psychischen Erkrankungen.

BACKGROUND: Loneliness is a widespread phenomenon, and it is associated with a variety of health problems and diseases. Unemployed individuals diagnosed with a mental illness (UMIs) are at a high risk of experiencing loneliness, with serious repercussions for their health and vocational rehabilitation. With this study we wanted to better understand the associations between sociodemographic variables, mental health, and loneliness in UMIs. METHODS: We analyzed the data of 526 unemployed individuals that received means-tested benefits as well as at least one psychiatric diagnosis. Data were collected between September 2020 and September 2023. We conducted two robust regressions with loneliness (University of California, Los Angeles, Loneliness Scale, UCLA) as an outcome (N = 526). In the first regression, we used social network as a predictor (Lubben Social Network Scale, LSNS-6); in the second regression we included the additional predictors personal debt (yes/no), depression (Patient Health Questionnaire, PHQ-9), anxiety (Mini-Symptom-Checklist,MSCL), and somatization (MSCL) as well as age, gender, education, and living with a partner as control variables. RESULTS: We found a significant negative association between social network and loneliness, and a significant positive association between a high level of education, depression, anxiety, and loneliness. CONCLUSIONS: Unemployed individuals diagnosed with a mental illness in our sample exhibited high levels of depression, anxiety, somatization, and loneliness. The associations between social network, mental health, and loneliness that we found emphasize the importance of psychological screening and/or diagnostics, and they highlight areas for prevention.

Social networks and posttraumatic stress symptoms five to ten years after the Fukushima nuclear power plant disaster.

Since the 2011 Fukushima nuclear power plant accident, multiple social network disruptions have been reported among the community in Fukushima, while posttraumatic stress symptoms among the residents have persisted. In this study, we aimed to explore the influence of time and social networks on the recovery of posttraumatic stress symptoms based on longitudinal data from community residents in Fukushima, following up five to ten years after the nuclear power plant accident. We conducted five questionnaire surveys quasi-annually, the targets of which were randomly sampled 4900 non-evacuee community residents. In this study, the data of 1809 respondents who participated in at least one survey were used (36.9% of the initial target). Setting posttraumatic stress symptoms as the outcome, we examined the interaction between time and social network size using a mixed model, adjusting for sociodemographic characteristics and disaster-related events. Their interaction was statistically significant, and the posttraumatic stress symptoms of those with small social networks persisted, while those with larger social networks recovered. Maintaining and promoting social networks may contribute to mental health recovery after a nuclear disaster.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

An overview of environmental risk factors for type 2 diabetes research using network science tools.

Objective: Current studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology. Methods: The Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends. Results: The publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress-air pollutants-PM2.5-air pollutants; (2) calcium-metabolic syndrome-cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)-persistent organic pollutants (POPs)-obesity. Conclusions: Digital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.

The effect of family physician team processes on chronic disease management performance from a structural network perspective.

OBJECTIVE: The family physician team has become the core carrier for delivery primary health care in China. This study aimed to measure the effect of the network structural characteristics of family physician team processes on health performance. Strategic recommendations for optimizing the family physician team processes with a view to improving performance were presented. METHODS: A cross-sectional survey was conducted from October to December 2021 in Qianjiang in Hubei Province and Changsha in Hunan Province. Task performance, contextual performance, social networks, and sociodemographic characteristics were collected. Social network analysis was conducted to calculate density and centralization, then hierarchical linear regression analysis was employed to explore the relationship between the network structural characteristics of family physician team processes and performance. RESULTS: In total, 88 family physician teams attended in this investigation. The transition processes of family physician team showed a distinctive low density (0.272 ± 0.112), high centralization (0.866 ± 0.197) network structure. For family physician team, the density of action processes significantly and positively affected task performance (B = 0.600, P < 0.05); the centralization of action processes positively affected task performance (B = 0.604, P < 0.01); the density of action processes positively affected contextual performance (B = 0.545, P < 0.01); the density of interpersonal processes significantly and positively affected contextual performance (B = 0.326, P < 0.05). CONCLUSION: The network density and centralization of family physician team processes have positive effects on chronic disease management performance. The results from this study help to enhance our conceptual understanding of social network and its implications for team-dynamics. Optimizing family physician team processes is an effective way to strengthen the construction of family physician team and promote the quality and efficiency of family physician-contracted service. It is recommended to strengthen the management of team processes, enhance the internal collaboration mechanism, and optimize the centralized network structure of family physician team.