A Social Network Analysis of a Multi-sector Service System for Intimate Partner Violence in a Large US City

About one in four women in the US report having experienced some form of intimate partner violence (IPV) during their lifetime and an estimated 15.5 million children live in families in which IPV occurred in the past year. Families of young children with IPV experiences often face complex needs and require well-coordinated efforts among service providers across social and health sectors. One promising partnership aims to support pregnant and parenting IPV survivors through coordination between IPV agencies and community-based maternal and early childhood home visiting programs. This study used social network analysis (SNA) to understand the interconnectedness of the system of IPV prevention and intervention for families with young children in a large US city. The SNA included 43 agencies serving this population across various service domains spanning IPV, legal, maternal and child health, and public benefit programs. An SNA survey collected data on four forms of collaboration between agencies, including formal administrative relationship, referral reciprocity, case consultation, and shared activities in community committees/organizing bodies. Density and centrality were the primary outcomes of interest. A community detection analysis was performed as a secondary analysis. The overall level of interconnectedness between the 43 responding agencies was low. Making referrals to each other was the most common form of collaboration, with a network density of 30%. IPV agencies had the highest average number of connections in the networks. There was a high level of variation in external collaborations among home visiting agencies, with several home visiting agencies having very few connections in the community but one home visiting program endorsing collaborative relationships with upwards of 38 partner agencies in the network. In serving families at risk for IPV, home visiting agencies were most likely to have referral relationships with mental health provider agencies and substance use disorder service agencies. A community detection analysis identified distinct communities within the network and demonstrated that certain agency types were more connected to one another while others were typically siloed within the network. Notably, the IPV and home visiting communities infrequently overlapped. Sensitivity analyses showed that survey participants' knowledge of their agencies' external collaborations varied by their work roles and agencies overall had low levels of consensus about their connectedness to one another. We identified a heterogeneous service system available to families of young children at-risk for or experiencing IPV. Overall inter-agency connectedness was low, with many siloed agencies and a lack of shared knowledge of community resources. Understanding current collaborations, silos, and centrality of agencies is an effective public health tool for allocating scarce resources across diverse service sectors to efficiently improve the system serving families experiencing IPV.

Healthcare social network research and the ECHO model™: Exploring a community of practice to support cultural brokers and transfer cultural knowledge

BACKGROUND

Project ECHO® networks at Children's Health Queensland Hospital and Health Service (CHQHHS) are communities of practice designed to mitigate services and systems fragmentation by building collaborative partnerships addressing priority child and youth health needs. Aboriginal and Torres Strait Islander people experience the negative impacts of fragmentation in addition to historical challenges of absent or culturally inappropriate health services. Access to culturally safe and responsive services can be improved by engaging Aboriginal and Torres Strait Islander Health Workers and similar roles in an online community of practice, supporting the integration of cultural and clinical knowledge and self-determination of Aboriginal and Torres Strait Islander consumers in decisions affecting their health. Analysing professional support networks and knowledge sharing patterns helps identify enablers and barriers to partnerships. Using social network research, the multilevel network inclusive of ECHO network members and their colleagues was studied to identify interdisciplinary and cross-sector advice exchange patterns, explore the position of cultural brokers and identify common relational tendencies.

METHODS

Social network theories and methods informed the collection of network data and analysis of advice-seeking relationships among ECHO network members and their nominees. Registered members from two ECHO networks were invited to complete the Qualtrics survey. Networks analysed comprised 398 professionals from mainstream health, Aboriginal and Torres Strait Islander Community Controlled Health Organisation, education, disability and child safety service settings.

RESULTS

Brokers were well represented, both those who hold knowledge brokerage positions as well as cultural brokers who incorporate clinical and cultural knowledge enabling holistic care for Aboriginal and Torres Strait Islander patients (38 individuals, 17% of network). Professionals who occupy brokerage positions outside the ECHO network tend to be more connected with co-members within the network.

CONCLUSIONS

This study is the first application of contemporary social network theories and methods to investigate an ECHO network. The findings highlight the connectivity afforded by brokers, enabling the coordination and collaboration necessary for effective care integration. Inclusion of cultural brokers in an ECHO network provides sustained peer group support while also cultivating relationships that facilitate the integration of cultural and clinical knowledge.

Agonistic interactions and social behaviors in the Saharan Dorcas gazelle (Gazella dorcas neglecta): Using social network analysis to evaluate relationships and social structure in captive male groups

Social interactions, including agonistic behavior, are very important for the management and welfare of individuals forming groups in captivity. One of the main concerns for the stability and durability of adult male groups is a noticeable level of intraspecific aggression. This study comprises a Social Network Analysis to illustrate social structure in different groups of captive Saharan Dorcas gazelles (Gazella dorcas neglecta). Our main objectives were to examine the relationship between agonistic, affiliative, and association networks and their reciprocity, assessing also whether the agonistic networks can conform to a linear dominance hierarchy. For these purposes, we recorded the behavior of 23 adult males organized in five herds, three composed only of Saharan Dorcas gazelle males and two mixed herds in which there were also Mhorr gazelle males (Nanger dama mohor). Observations were carried out during 295 h through scan sampling. We found no correlation between the affiliative and association networks in any group, although there was a significant correlation between the agonistic and association networks in mixed-species groups which was not present in single-species groups. Overall, there was no consistent reciprocity in either affiliative nor agonistic networks and none of the agonistic networks showed a linear structure. These results indicate that affiliative behavior in Saharan Dorcas gazelles offers distinctive and valuable information about the bonds between individuals, however, their dominance structure is far more complex than previously thought. As information provided by affiliative and proximity behaviors is different in this species, we suggest considering affiliative interactions to stablish affinity between individuals. Evaluating different social behaviors and not only agonistic interactions in later studies, is also recommended to develop a more accurately daily management in zoos that guarantee group stability and individuals' welfare, which will improve the conservation of captive populations.

Faculty members as academic knowledge brokers in Iran's health sector: a social network analysis study

BACKGROUND

Interaction between researchers and policymakers is an essential factor to facilitate the evidence-informed policymaking. One of the effective ways to establish this relationship and promote evidence-informed policymaking is to employ people or organizations that can play the role of knowledge brokers. This study aims to analyze the communication network and interactions between researchers and policymakers in Iran's health sector and identify key people serving as academic knowledge brokers.

METHODS

This study was a survey research. Using a census approach, we administered a sociometric survey to faculty members in the health field in top ten Iranian medical universities to construct academic-policymaker network using social network analysis method. Network maps were generated using UCINET and NetDraw software. We used Indegree Centrality, Outdegree Centrality, and Betweenness Centrality indicators to determine knowledge brokers in the network.

RESULTS

The drawn network had a total of 188 nodes consisting of 94 university faculty members and 94 policymakers at three national, provincial, and university levels. The network comprised a total of 177 links, with 125 connecting to policymakers and 52 to peers. Of 56 faculty members, we identified four knowledge brokers. Six policymakers were identified as key policymakers in the network, too.

CONCLUSIONS

It seems that the flow of knowledge produced by research in the health field in Iran is not accomplished well from the producers of research evidence to the users of knowledge. Therefore, it seems necessary to consider incentive and support mechanisms to strengthen the interaction between researchers and policymakers in Iran's health sector.

Coordination and modularization: the experience of the joint prevention and control mechanism to COVID emergencies in China

Background

The integration of disparate emergency resources and the improvement of emergency response teamwork are the underlying trends and shared requirements for building resilience in an era of multiple global public health crises.

Objective

This study investigated the emergency response with emergency collaboration networks of each functional module and the overall Joint Epidemic Prevention and Control Mechanism (JPCM) network in China's COVID outbreak prevention and control.

Methods

The study employed a scholarly framework of "the integration of JPCM coordination and emergency collaborative modularization" to explore the attributes of JPCM using social network analysis. The data were obtained from administrative records from JPCM's official website, spanning January 2020 to December 2022.

Results

The study examined the JPCM coordination and found several functional working modules of JPCM, such as Interrupt Spread, Manage Supply, Medical Rescue, Restore Work and Production, and Implement Responsibility modules. The network structure indicators showed that the Manage Supply module had the most extensive network connectivity, the shortest communication distance, and the most consistent collaboration. The E-I index of the overall JPCM network and the Manage Supply network were - 0.192 and - 0.452, respectively (at p < 0.001 and p < 0.05), indicating more internal relationships than external relationships. The E-I index of the Medical Rescue and Implement Responsibility collaboration networks were 0.122 and 0.147, respectively (at p < 0.001 and p < 0.05), indicating more external relationships than internal relationships. The QAP regression analysis showed that the most vital driver on the overall JPCM network was the Interrupt Spread module, followed by the Implement Responsibility and Medical Rescue modules.

Discussion

The Interrupt Spread module initiated emergency coordination with most departments and agencies. The Manage Supply module ensured the flow of medical supplies and survival essentials, while the Medical Rescue module addressed the core aspects of the health emergency response. The Restore Work and Production module repaired the halt in production and livelihoods caused by the outbreak, strengthening and developing emergency coordination and roles across emergency organizations. The Implement Responsibility module provided more heterogeneous emergency response resources for the overall JPCM coordination, complementing the COVID cross-organizational emergency response coordination.

Conclusion

The study on the JPCM case in China improves public health emergency management and aids informed decision-making.

Utilising qualitative data for social network analysis in disaster research: opportunities, challenges, and an illustration

An abundance of unstructured and loosely structured data on disasters exists and can be analysed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. It discusses two types of networks, each with a relevant major topic in disaster research-that is, (i) whole network approaches to emergency management networks and (ii) personal network approaches to the social support of survivors-and four usable forms of qualitative data. This paper explains five opportunities afforded by these approaches, revolving around their flexibility and ability to account for complex network structures. Next, it presents an empirical illustration that extends the authors' previous work examining the sources and the types of support and barrier experienced by households during long-term recovery from Hurricane (Superstorm) Sandy (2012), wherein quantitative social network analysis was applied to two qualitative datasets. The paper discusses three challenges associated with these approaches, related to the samples, coding, and bias.

Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer

Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%-4% and 7-8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models' performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.

Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review

PURPOSE

Social network analysis (SNA) is a theoretical framework and analytical approach used to study relationships among individuals and groups. While SNA has been employed by many disciplines to understand social structures and dynamics of interpersonal relationships, little is known about its use in medical education. Mapping and synthesizing the scope of SNA in undergraduate and postgraduate medical education can inform educational practice and research.

METHOD

This scoping review was based on searches conducted in Medline, Embase, Scopus, and ERIC in December 2020 and updated in March 2022. After removal of duplicates, the search strategy yielded 5,284 records, of which 153 met initial inclusion criteria. Team members conducted full-text reviews, extracted relevant data, and conducted descriptive and thematic analyses to determine how SNA has been used as a theoretical and analytical approach in undergraduate and postgraduate medical education.

RESULTS

Thirty studies, from 11 countries, were retained. Most studies focused on undergraduate medical students, primarily in online environments, and explored students' friendships, information sharing, and advice seeking through SNA. Few studies included residents and attending staff. Findings suggested that SNA can be a helpful tool for monitoring students' interactions in online courses and clinical clerkships. SNA can also be used to examine the impact of social networks on achievement, the influence of social support and informal learning outside the classroom, and the role of homophily in learning. In clinical settings, SNA can help explore team dynamics and knowledge exchange among medical trainees.

CONCLUSIONS

While SNA has been underutilized in undergraduate and postgraduate medical education, findings indicate that SNA can help uncover the structure and impact of social networks in the classroom and the clinical setting. SNA can also be used to help design educational experiences, monitor learning, and evaluate pedagogical interventions. Future directions for SNA research in medical education are described.

Describing nurses' communication about evidence-based practice change: A social network analysis of hospital nurses

BACKGROUND

Nurses play a critical role in providing evidence-based, high-quality care to optimize patient outcomes. Models from implementation science suggest social networks may influence the adoption of evidence-based practices (EBPs). However, few studies have examined this relationship among hospital nurses. Social network analysis (SNA) mathematically evaluates patterns of communication, a critical step in implementation. Exploring hospital nurses' communication networks may provide insight into influences on the adoption of EBPs.

AIMS

This study aimed to describe complete communication networks of hospital nurses for practice changes on inpatient units, including upper level nursing administrators.

METHODS

This descriptive, exploratory, cross-sectional study used SNA on two inpatient units from one hospital. A sociometric survey was completed by nurses (unit to executive level) regarding communication frequency about practice changes. Network-level density, diameter, average path length, centralization, and arc reciprocity were measured. Attribute data were used to explore subnetworks.

RESULTS

Surveys from 148 nurses on two inpatient adult intensive care units (response rates 90% and 98%) revealed high communication frequency. Network measures were similar across the two units and among subnetworks. Analysis identified central (charge nurses and nurse leaders) and peripheral members of the network (new-to-practice nurses). Subnetworks aligned with the weekend and shift worked.

LINKING EVIDENCE TO ACTION

Established communication channels, including subnetworks and opinion leaders, should be used to maximize and optimize implementation strategies and facilitate the uptake of EBPs. Future work should employ SNA to measure the impact of communication networks on promoting the uptake of EBP and to improve patient outcomes.

The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data

BACKGROUND

 Social media (SM) was essential in promoting physical activity during the COVID-19 pandemic, especially among people confined to their homes. Joe Wicks, a fitness coach, became particularly popular on SM during this time, posting daily workouts that millions of people worldwide followed.

OBJECTIVE

 This study aims to investigate the influence of Joe Wicks on SM and the impact of his content on physical activity levels among the public.

METHODS

 We used NodeXL Pro (Social Media Research Foundation) to collect data from X (formerly Twitter) over 54 days (March 23, 2020, to May 15, 2020), corresponding to the strictest lockdowns in the United Kingdom. We collected 290,649 posts, which we analyzed using social network analysis, thematic analysis, time-series analysis, and location analysis.

RESULTS

 We found that there was significant engagement with content generated by Wicks, including reposts, likes, and comments. The most common types of posts were those that contained images, videos, and text of young people (school-aged children) undertaking physical activity by watching content created by Joe Wicks and posts from schools encouraging pupils to engage with the content. Other shared posts included those that encouraged others to join the fitness classes run by Wicks and those that contained general commentary. We also found that Wicks' network of influence was extensive and complex. It contained numerous subcommunities and resembled a broadcast network shape. Other influencers added to engagement with Wicks via their networks. Our results show that influencers can create networks of influence that are exhibited in distinctive ways.

CONCLUSIONS

Our study found that Joe Wicks was a highly influential figure on SM during the COVID-19 pandemic and that his content positively impacted physical activity levels among the public. Our findings suggest that influencers can play an important role in promoting public health and that government officials should consider working with influencers to communicate health messages and promote healthy behaviors. Our study has broader implications beyond the status of fitness influencers. Recognizing the critical role of individuals such as Joe Wicks in terms of health capital should be a critical area of inquiry for governments, public health authorities, and policy makers and mirrors the growing interest in health capital as part of embodied and digital experiences in everyday life.

Social associations and alcohol consumption in an Australian community sample: An egocentric social network analysis

OBJECTIVE

This study examined how social associations from a person's social network may be associated with their own alcohol consumption.

METHOD

Alcohol consumption behavior was examined among the social networks of 784 survey respondents (54% female, Mage = 35.3 years), using egocentric social network analysis. Participants (egos) were recruited via a panel aggregator and completed an online survey about the frequency of their alcohol consumption and that of the 20 most influential people in their lives (alters). The survey also explored who these alters were (family, friends, work colleagues) and the interrelationships among these alters.

RESULTS

Egos who consumed alcohol, or consumed alcohol more frequently, were surrounded by more alters who also drank alcohol and felt closer (had stronger ties) to these alters. These relationships remained statistically significant when controlling for demographic and other variables. The social networks of those who consumed alcohol more frequently were more densely intertwined.

CONCLUSIONS

Alcohol may serve to initiate social connections and be a "social glue" that reinforces relationships. These strong social associations present a potential barrier to individuals who wish to reduce their alcohol consumption because they have few close social connections who do not drink alcohol (or who do so infrequently), and their highly interconnected social networks make it difficult to socialize only with those who do not drink frequently. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social network analysis of the CaRE2 health equity center: Team science in full display

Cancer health disparities that exist in the Black or African American and Hispanic or Latino/x communities are scientific challenges, yet there are limited team science approaches to mitigate these challenges. This article's purpose is to evaluate the team science collaborations of the National Institutes of Health-funded Florida-California Cancer Research, Education & Engagement (CaRE2 ) Center partnership underscoring the inclusion of multidisciplinary team members and future under-represented minority (URM) cancer researchers. To understand our collaborative efforts, we conducted a social network analysis (SNA) of the CaRE2 Center partnership among University of Florida, Florida A&M University, and University of Southern California with data collected via the dimensions.ai application programming interface. We downloaded metadata for all publications associated with dimensions.ai IDs. The CaRE2 collaboration network increased over time as evidenced by accruing more external collaborators and more publishing of collaborative works. Degree centrality of key personnel was stable in each wave of the networks. CaRE2 key personnel averaged a total of 60.8 collaborators in 2018-2019 (SD = 57.4, minimum = 3, maximum = 221), and 65.8 collaborators in 2020-2021 (SD = 56.06, minimum = 4, maximum = 222). Betweenness was largely stable across all groups and waves. We observed a steady decline in transitivity, the probability that a pair of CaRE2 co-authors shared a third co-author, from 0.74 in 2018 to 0.47 in 2022. The SNA findings suggest that the CaRE2 Center partnership's publications show growth in team science collaborations with the inclusion of multidisciplinary team members from the three partner institutions and future URM cancer researchers who were mentored as trainees and early-stage investigators.

Representation of the hierarchical and functional structure of an ambulatory network of medical consultations through Social Network Analysis, with an emphasis on the role of medical specialties

BACKGROUND

Ambulatory Health Care Networks (Amb-HCN) are circuits of patient referral and counter-referral that emerge, explicitly or spontaneously, between doctors who provide care in their offices. Finding a meaningful analytical representation for the organic and hierarchical functioning of an Amb-HCN may have managerial and health policymaking implications. We aimed to characterize the structural and functional topology of an Amb-HCN of a private health insurance provider (PHIP) using objective metrics from graph theory.

METHODS

This is a cross-sectional quantitative study with a secondary data analysis study design. A Social Network Analysis (SNA) was conducted using office visits performed between April 1, 2021 and May 15, 2022, retrieved from secondary administrative claim databases from a PHIP in Belo Horizonte, Southeastern Brazil. Included were beneficiaries of a healthcare plan not restricting the location or physician caring for the patient. A directional and weighted network was constructed, where doctors were the vertices and patient referrals between doctors, within 7-45 days, were the network edges. Vertex-level SNA measures were calculated and grouped into three theoretical constructs: patient follow-up (aimed at assessing the doctor's pattern of patient follow-up); relationship with authorities (which assessed whether the doctor is an authority or contributes to his or her colleague's authority status); and centrality (aimed at positioning the doctor relative to the network graph). To characterize physician profiles within each dimension based on SNA metrics results, a K-means cluster analysis was conducted. The resulting physician clusters were assigned labels that sought to be representative of the observed values of the vertex metrics within the clusters.

FINDINGS

Overall, 666,263 individuals performed 3,863,222 office visits with 4,554 physicians. A total of 577 physicians (12.7%) had very low consultation productivity and contributed very little to the network (i.e., about 1.1% of all referrals made or received), being excluded from subsequent doctor profiles analysis. Cluster analysis found 951 (23.9%) doctors to be central in the graph and 1,258 (31.6%) to be peripheral; 883 (22.2%) to be authorities and 266 (6.7%) as seeking authorities; 3,684 (92.6%) mostly shared patients with colleagues, with patient follow-up intensities ranging from weak to strong. Wide profile dispersion was observed among specialties and, more interestingly, within specialties. Non-primary-care medical specialties (e.g., cardiology, endocrinology etc.) were associated with central profile in the graph, while surgical specialties predominated in the periphery, along with pediatrics. Only pediatrics was associated with strong and prevalent (i.e., low patient sharing pattern) follow-up. Many doctors from internal medicine and family medicine had unexpectedly weak and shared patient follow-up profiles. Doctor profiles exhibited pairwise relationships with each other and with the number of chronic comorbidities of the patients they treated. For example, physicians identified as authorities were frequently central and treated patients with more comorbidities. Ten medical communities were identified with clear territorial and specialty segregation.

CONCLUSIONS

Viewing the Amb-HCN as a social network provided a topological and functional representation with potentially meaningful and actionable emerging insights into the most influential actors and specialties, functional hierarchies, factors that lead to self-constituted medical communities, and dispersion from expected patterns within medical specialties.

Detecting Variation in Clinical Practice Patterns for Geriatric Trauma Care Using Social Network Analysis

OBJECTIVE

To characterize hospital-level professional networks of physicians caring for older trauma patients as a function of trauma patient age distribution.

BACKGROUND

The causal factors associated with between-hospital variation in geriatric trauma outcomes are poorly understood. Variation in physician practice patterns reflected by differences in professional networks might contribute to hospital-level differences in outcomes for older trauma patients.

METHODS

This is a population-based, cross-sectional study of injured older adults (age 65 or above) and their physicians from January 1, 2014, to December 31, 2015, using Health Care Cost and Utilization Project inpatient data and Medicare claims from 158 hospitals in Florida. We used social network analyses to characterize the hospitals in terms of network density, cohesion, small-worldness, and heterogeneity, then used bivariate statistics to assess the relationship between network characteristics and hospital-level proportion of trauma patients who were aged 65 or above.

RESULTS

We identified 107,713 older trauma patients and 169,282 patient-physician dyads. The hospital-level proportion of trauma patients who were aged 65 or above ranged from 21.5% to 89.1%. Network density, cohesion, and small-worldness in physician networks were positively correlated with hospital geriatric trauma proportions ( R =0.29, P <0.001; R =0.16, P =0.048; and R =0.19, P <0.001, respectively). Network heterogeneity was negatively correlated with geriatric trauma proportion ( R =0.40, P <0.001).

CONCLUSIONS

Characteristics of professional networks among physicians caring for injured older adults are associated with the hospital-level proportion of trauma patients who are older, indicating differences in practice patterns at hospitals with older trauma populations. Associations between interspecialty collaboration and patient outcomes should be explored as an opportunity to improve the treatment of injured older adults.

Investigating influential factors and mechanisms of surgical resident well-being using social network analysis

BACKGROUND

Burnout and depression in General Surgery residents affect patient care and drive attrition. Few impactful interventions exist, in part because little is known about influential drivers and mechanistic relationships.

METHODS

Residents from 16 general surgery programs completed published well-being scales in January 2021. Social network analysis demonstrated influential relationships between factors, and path analysis revealed drivers of burnout and depression.

RESULTS

300 residents completed the survey (34% response rate). Workplace demand and mindfulness influenced depressive symptoms and emotional exhaustion, mediated by perceived stress. Mindfulness increased personal accomplishment, mediated by psychological well-being (p ​< ​0.05 for all results). All mediated effects were greater than direct effects.

CONCLUSIONS

This study yielded a quantitative conceptual model of mechanistic relationships affecting well-being in surgical residency, identifying stress and psychological well-being (measuring sense of purpose) as central mediators, and triangulating workplace demand and mindfulness as potentially high-yield interventional targets for reducing burnout and depression in surgical residency.

Team communication patterns during real and simulated trauma resuscitation-a social network analysis

In trauma teams, coordination can be established through a centralised leader. The team can also use a decentralised strategy. In this descriptive study of video-recorded trauma resuscitations, using quantification of qualitative data, Social Network analysis of all real-time communications of eight in-real-life (IRL) and simulated trauma teams explained team social structure. The communication network structures in the simulated scenarios were more centralised using individually directed speech and had a high proportion of communication to update all team members. Such a structure might be the result of work performed in a complexity-stripped simulation environment where simplified task-executions required less interactions, or from work revolving around a deteriorating patient, imposing high demands on rapid decision-making and taskwork. Communication IRL was mostly decentralised, with more variability between cases, possibly due to unpredictability of the IRL case. The flexibility to act in a decentralised manner potentiates adaptability and seems beneficial in rapidly changing situations.Practitioner summary: Efficient collaboration in trauma teams is essential. Communication in in-real-life and simulated trauma teams was analysed using social network analysis. The simulation teams were overall more centralised compared to the IRL teams. The flexibility to act decentralised seems beneficial for emergency teams as it enables adaptability in unpredictable situations.

The Intersection of Age and HIV Status for Black Sexual Minority Men (BSMM): A Social Network Analysis

Advancements in treatment have resulted in increased life expectancy for individuals living with HIV. However, there is a dearth of literature focused on the intersection of age and HIV status, particularly for older Black sexual minority men (SMM) who are disproportionately impacted by HIV. This study aimed to examine the intersecting effect of age and HIV status on Black SMM social networks in a sample from the Social Network and Prevention Study. Participants were 18 years of age or older, identified as cis-gender Black or African American, self-identified as SMM, reported unprotected sex within the past six months, and resided in Baltimore city or a surrounding county. The sample was divided into four categories by age (e.g., young, mature) and HIV status (e.g., positive, negative). Of the sample, 167 men were (a) Young & HIV negative, 116 men were (b) Young & HIV positive, 44 men were (c) Mature & HIV negative, and 42 men were (d) Mature & HIV positive. Among the four groups, mature men who were HIV positive had the fewest average number of alters who knew they were SMM. There was also overlap in the range of age of sexual partners across the four groups, ranging from 17 to 53.5 (Group 1), 20-60 (Group 2), 29.5-60 (Group 3), and 23-63 (Group 4) years of age. Although a cross-sectional analysis, our findings suggest value in life course-informed research and practice for providing HIV and sexual health programming. Focus on services provided by community organizations may help mitigate existing disparities.

Feasibility of social network analysis to study outcomes of children with medical complexity

Since most care for children with medical complexity (CMC) is delivered daily in communities by multiple caregiving individuals, that is, caregiving networks, tools to assess and intervene across these networks are needed. This study evaluated the feasibility of applying social network analysis (SNA) to describe caregiving networks. Because hospitalization is among the most frequently used outcomes for CMC, exploratory correlations between network characteristics and CMC hospital use were evaluated. Within 3 weeks, the goal network enrollment was achieved, and all feasibility measures were favorable. Network characteristics correlated with hospital use, that is, smaller, denser networks, with more closed-loop communication correlated with fewer hospital days. Networks with more professional caregivers also correlated with fewer hospital days. SNA is a feasible tool to study CMC caregiving networks. Preliminary data support rigorous hypothesis testing using SNA methods. Network-based interventions to improve CMC health may be an important future direction.

The Pulse of Long COVID on Twitter: A Social Network Analysis

BACKGROUND

Long coronavirus disease (COVID) is a complex and multifaceted health condition with a range of severe symptoms that can last for weeks or even months after the acute phase of the illness has passed. Employing social network analysis (SNA) can rapidly provide significant health information to communities related to long COVID. This study aimed to identify the key themes, most influential users, and overall sentiments in the Twitter discourse on long COVID.

METHODS

Data were collected from a Twitter search with the specific keywords "long COVID" from December 1, 2022, to February 22, 2023, using NodeXL Pro. Visualizations, including network graphs and key influencers, were created using Gephi, and sentiment analysis was conducted with Azure Machine.

RESULTS

In total, 119,185 tweets from 94325 users were related to long COVID. Top influencers include medical professionals, researchers, journalists, and public figures, with news media platforms as primary information sources; the most common hashtag was #longCOVID, indicating that it is a significant issue of concern among the Twitter community. In the sentiment analysis, most tweets were negative.

CONCLUSION

The study highlights the importance of critically evaluating information shared by influential users and seeking out multiple sources of information when making health-related decisions. In addition, it emphasizes the value of examining social media conversations to understand public discourse on long COVID and suggests that future researchers could explore the role of social media in shaping public perceptions and behaviors related to health issues. Strategies for enhancing scientific journal engagement and influence in online discussions are discussed as well.

Evolution of global scientific collaboration in mRNA vaccine research: Insights from bibliometric and social network analysis (2010~2023)

The field of mRNA vaccines has witnessed rapid development in recent years, leading to significant changes in global scientific collaboration. In this study, a bibliometric and social network analysis was conducted to reveal the evolution of global scientific collaboration in mRNA vaccine research. Altogether 6974 articles published since 2010 were retrieved and categorized into Period 1 (2010-2019), Period 2 (2020-2021) and Period 3 (2022-2023). During Period 2 and 3, there was a significant rise in the proportion of publications involving domestic inter-institutional cooperation (42.0%, 54.0% and 59.1%, respectively in Period 1, 2, and 3), while a significant decrease in international cooperation (32.1%, 23.7% and 21.0%). More countries participated in international collaboration during Period 2 and 3, with the US, the UK and Germany remaining top three throughout all periods, while some other countries like Italy, Japan, and China experiencing significant shifts. Significant correlations between collaboration type and publication impact and between geographical distance and collaborative publication counts were detected. Furthermore, significant changes in research focuses and institutions that are major contributors in the mRNA vaccine development have been observed. In conclusion, the mRNA vaccine field has experienced rapid development over the past decade, with significant evolutions of global scientific collaboration detected in our study.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Scientific and academic contributions of professor Enric Brillas through an analysis social network analysis and data science

This work describes the scientific and academic contributions of Professor Enric Brillas through the analysis of Social Network Analysis and data science. The study examines the research collaborations and co-authorship networks of Professor Brillas, indicating his active engagement and up-to-date collaborations with key co-authors, including Ignasi Sirés and Pere.L. Cabot. The analysis also reveals Professor Brillas' significant research focus on water treatment and related concepts such as oxidation-reduction, Fenton reactions, photoelectro-Fenton, and electrocatalysis. Furthermore, the most cited and recent articles by Professor Brillas are identified and discusses. Overall, the research demonstrates Professor Brillas' notable contributions to the field of electrochemical water treatment and highlights his ongoing research and collaborations in this area.

A Mixed-Method Social Network Analysis of Low-Income Diverse Older Volunteers

Although volunteering has been associated with numerous social benefits for diverse older adults, there is little information on how they establish relationships within a multicultural volunteering program outside of their co-ethnic communities. This convergent mixed-method social network study adopts the bonding and bridging social capital theory to explore the structures and dynamics of social interactions within a multicultural volunteer program. Low-income Russian, Khmer, Somali, Nepali, and English-speaking older volunteers in the Senior Companions Program (SCP) in a Midwest metropolitan (N = 83) participated in the surveys and focus groups. Homophily coefficients (r) show that the SCP volunteers mostly interacted with people who identified with the same nationality (r = .86), race (r = .87), and gender (r = .50). Qualitative results suggested that volunteers strengthened their social networks through within-cultural social bonding while appreciating opportunities for cross-cultural social bridging. Compared with within-cultural social bonding, cross-cultural social bridging in multicultural volunteer programs require intentional facilitation, resources, and organizational commitment.

Influence of Disclosed and Undisclosed Funding Sources in Tobacco Harm Reduction Discourse: A Social Network Analysis

INTRODUCTION

Tobacco harm reduction (THR) discourse has been divisive for the tobacco control community, partially because it sometimes aligns public health and tobacco industry interests. Industry funding is contentious as it influences study outcomes, and is not always disclosed in scientific publications. This study examines the role of disclosed and undisclosed industry support on THR publications via social network analysis.

METHODS

We reviewed 826 English-language manuscripts (1992-2016) to determine disclosed and undisclosed industry (pharmaceutical, tobacco, and e-cigarette) and non-industry (including government) support received by 1405 authors. We used social network analysis to identify the most influential authors in THR discourse by assessing the number of their collaborators on publications, the frequency of connecting other authors in the network, and tendency to form groups based on the presence of sponsorship disclosures, sources of funding, and THR stance.

RESULTS

About 284 (20%) out of 1405 authors were supported by industry. Industry-sponsored authors were more central and influential in the network: with twice as many publications (Median = 4), 1.25 as many collaborators on publications (Median = 5), and higher likelihood of connecting other authors and thus having more influence in the network, compared to non-industry-sponsored authors. E-cigarette industry-sponsored authors had the strongest association with undisclosed industry support.

CONCLUSIONS

Authors with industry support exerted a stronger influence on the THR scientific discourse than non-industry-supported authors. Journals should continue adhering to strict policies requiring conflicts of interest disclosures. An increase in public health spending on tobacco control research may be necessary to achieve funding parity.

Can participatory processes lead to changes in the configuration of local mental health networks? A social network analysis

Systems modeling offers a valuable tool to support strategic decision-making for complex problems because it considers the causal inter-relationships that drive population health outcomes. This tool can be used to simulate policies and initiatives to determine which combinations are likely to deliver the greatest impacts and returns on investment. Systems modeling benefits from participatory approaches where a multidisciplinary stakeholder group actively engages in mapping and contextualizing causal mechanisms driving complex system behaviors. Such approaches can have significant advantages, including that they may improve connection and coordination of the network of stakeholders operating across the system; however, these are often observed in practice as colloquial anecdotes and seldom formally assessed. We used a basic social network analysis to explore the impact on the configuration of the network of mental health providers, decision-makers, and other stakeholders in Bogota, Colombia active in a series of three workshops throughout 2021 and 2022. Overall, our analysis suggests that the participatory process of the systems dynamics exercise impacts the social network's structure, relationships, and dynamics.

A social network analysis of perpetrators of child maltreatment reported by youth in foster care

OBJECTIVE

Prior research on child maltreatment has focused on distinct features of maltreatment (type, severity, chronicity) important for youth outcomes, yet perpetrators of child maltreatment reported by youth have gone largely unstudied. The present study examines connections between perpetrators, the total number and type of perpetrators reported, and the frequency at which each type of perpetrator was reported across 24 relationship types to provide a foundation for future research seeking to provide comprehensive measurement of perpetrator profiles.

METHODS

Data from 503 youth in foster care (8-21 years old) were collected through the Studying Pathways to Adjustment and Resilience in Kids (SPARK) Project. Youth reported on their history of physical, sexual, and psychological maltreatment. Social Network Analysis (SNA) was used to visualize links between perpetrators within maltreatment type and paired samples t-tests were used to compare differences between network edge weights.

RESULTS

Full sample SNA results were highly interconnected and variable across maltreatment types. Biological parents and peers were the most common perpetrators of physical and psychological abuse with peers and non-family adults being most common for sexual abuse. Family and community member groupings were most distinct in the physical and psychological abuse networks whereas in the sexual abuse network, ties between perpetrators were more equidistant.

CONCLUSIONS

Differences exist in perpetrator profiles across maltreatment types, adding a layer of complexity to how maltreatment experiences are captured, and variability in profiles might provide insight to differing youth outcomes. Understanding individual youth perpetrator profiles could be used to inform foster care placements and reduce the risk of revictimization.

A Social Network Analysis of Influences on Residents' Value-Based Decisions

PURPOSE

To ensure a value-based health care system, it is becoming increasingly important that residents are trained in making value-based decisions. This study explored the social network influencing residents' value-based decisions.

METHOD

To explore the social network influencing residents' value-based decisions, the authors used a semistructured individual and mini-group interviewing approach and participatory visual mapping. In total, 17 residents across 13 different specialties were interviewed from the southeastern postgraduate medical education and training region of the Netherlands, May-November 2021. Two researchers independently coded the transcribed data using an integrated inductive thematic approach. Subsequently, social network analysis was used to visualize the results.

RESULTS

Residents indicated that their value-based decisions were influenced by direct actors who influenced decisions related to patients and indirect actors who shaped decisions related to patients without directly modifying them. Different interaction-aspects (i.e., personal, situational, and institutional) further affected residents' ability to make value-based decisions. Thus, residents' value-based decisions were a product of the interplay between various interactions with actors and different interaction-aspects. Residents defined value-based decisions differently, even within an interview.

CONCLUSIONS

These results suggest residents' value-based decisions are influenced by a multitude of actors, including hierarchically superior colleagues who can directly alter decisions and patients (and their families) and nurses with whom residents consider it important to maintain good relationships. In addition, more experienced actors, mainly from the medical and nursing profession, contribute most to learning. Furthermore, residents' value-based decisions are deeply underpinned by the hidden curriculum. However, many senior physicians may not have received sufficient training in the concept of value-based health care. Consequently, an approach of formally educating residents in value-based health care will likely have limited effects unless social influences in day-to-day clinical settings reinforce its importance.

Can peer effects explain prescribing appropriateness? a social network analysis

BACKGROUND

Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions.

METHODS

We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.

RESULTS

We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).

CONCLUSIONS

Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

Examining preconditions for integrated care: a comparative social network analysis of the structure and dynamics of strong relations in child service networks

BACKGROUND

To help ensure that children and families get the right support and services at the right time, strong and stable relationships between various child service organizations are vital. Moreover, strong and stable relationships and a key network position for gatekeepers are important preconditions for interprofessional collaboration, the timely and appropriate referral of clients, and improved health outcomes. Gatekeepers are organizations that have specific legal authorizations regarding client referral. However, it is largely unclear how strong relations in child service networks are structured, whether the gatekeepers have strong and stable relationships, and what the critical relations in the overall structure are. The aim of this study is to explore these preconditions for integrated care by examining the internal structure and dynamics of strong relations.

METHODS

A comparative case study approach and social network analysis of three inter-organizational networks consisting of 65 to 135 organizations within the Dutch child service system. Multiple network measures (number of active organizations, isolates, relations, average degree centrality, Lambda sets) were used to examine the strong relation structure and dynamics of the networks. Ucinet was used to analyze the data, with use of the statistical test: Quadratic Assignment Procedure. Visone was used to visualize the graphs of the networks.

RESULTS

This study shows that more than 80% of the organizations in the networks have strong relations. A striking finding is the extremely high number of strong relations that gatekeepers need to maintain. Moreover, the results show that the most important gatekeepers have key positions, and their strong relations are relatively stable. By contrast, considering the whole network, we also found a considerable measure of instability in strong relationships, which means that child service networks must cope with major internal dynamics.

CONCLUSIONS

Our study addressed crucial preconditions for integrated care. The extremely high number of strong relations that particularly gatekeepers need to build and maintain, in combination with the considerable instability of strong relations considering the whole network, is a serious point of concern that need to be managed, in order to enable child service networks to improve internal coordination and integration of service delivery.

The Role of Location in the Spread of SARS-CoV-2: Examination of Cases and Exposed Contacts in South Texas, Using Social Network Analysis

OBJECTIVE

This study sought to better understand the types of locations that serve as hubs for the transmission of COVID-19.

METHODS

Contact tracers interviewed individuals who tested positive for SARS-CoV-2 between November 2020 and March 2021, as well as the people with whom those individuals had contact. We conducted a 2-mode social network analysis of people by the types of places they visited, focusing on the forms of centrality exhibited by place types.

RESULTS

The most exposed locations were grocery stores, commercial stores, restaurants, commercial services, and schools. These types of locations also have the highest "betweenness," meaning that they tend to serve as hubs between other kinds of locations since people would usually visit more than 1 location in a day or when infected. The highest pairs of locations were grocery store/retail store, restaurant/retail store, and restaurant/grocery store. Schools are not at the top but are 3 times in the top 7 pairs of locations and connected to the 3 types of locations in those top pairs.

CONCLUSIONS

As the pandemic progressed, location hotspots shifted between businesses, schools, and homes. In this social network analysis, certain types of locations appeared to be potential hubs of transmission.

Relevance of national, regional and global virome projects on pandemics prediction, prevention, and control: a social network analysis of GVP-citing articles

BACKGROUND

The Global Virome Project (GVP) was proposed in 2018 as an evolution of the USAID PREDICT project and was presented as a "collaborative scientific initiative to discover zoonotic viral threats and stop future pandemics". The immediate response was mixed, with public health and scientific communities representatives showing skepticism, if not direct opposition.

OBJECTIVES

The economic, social, and health consequences of the coronavirus disease 2019 (COVID-19) pandemic demonstrated how unprepared the world was in the face of new pandemics. This paper analyses the impact of the GVP on the scientific and public health communities.

METHODS

Published scientific articles that cited the two 2018 seminal publications proposing the project were analysed using social network analysis methods.

FINDINGS

Encompassing the periods before and after the onset of the Covid-19 pandemic, the results indicate that (i) the concepts of the GVP have received more support than opposition in the scientific literature; (ii) its foundations should be updated to address the specific criticisms.

MAIN CONCLUSIONS

Shifting focus to national virome projects can provide tangible, regional benefits that can positively contribute towards a consensus on achieving a high level of preparedness for the ever-present possibility of the following global viral pandemic.

Racial/Ethnic disparities in the chains of morbid events leading to death: network analysis of US multiple cause of death data

Multiple-cause-of-death data have not yet been applied to the study of racial/ethnic differences in causal chains of events leading to death, nor they have been used to examine racial/ethnic disparities in cause-of-death certification. We use publicly available 2019 US death certificate data to reassemble chains of morbid events leading to death. From them, we construct and analyze directed multiple cause of death networks by race and sex of deaths aged 60+. Three perspectives to measure disparities are employed: (i) relative prevalence of cause-of-death-pairs, (ii) strength of associations between diseases, (iii) similarities in transition matrices. Non-Hispanic Blacks (NHB) had overall lower prevalence of cause of death pairs, Hispanics (HIS) were burdened more by alcohol-related mortality and Asian and Pacific Islanders (API) exceeded in transitions to cerebrovascular diseases. Lower similarity was observed in transitions to external causes of death, dementia and Alzheimer's disease, pulmonary heart diseases, interstitial respiratory diseases, and diseases of the liver. After excluding rare diseases, the similarity further decreased for ill-defined conditions, diabetes mellitus, other cardiovascular diseases, diseases of the pleura, and anemia. To sum up, races/ethnicities not only vary in structure and timing of death but they differ in morbid processes leading to death as well.

Adolescent alcohol use: use of social network analysis and cross-classified multilevel modeling to examine peer group, school, and neighborhood-level influences

Background: Neighborhood-, school-, and peer-contexts play an important role in adolescent alcohol use behaviors. Methodological advances permit simultaneous modeling of these contexts to understand their relative and joint importance. Few empirical studies include these contexts, and studies that do typically: examine each context separately; include contexts for the sole purpose of accounting for clustering in the data; or do not disaggregate by sex.Objectives: This study takes an eco-epidemiologic approach to examine the role of socio-contextual contributions to variance in adolescent alcohol use. The primary parameters of interest are therefore variance rather than beta parameters (i.e. random rather than fixed effects). Sex-stratified models are also used to understand how each context may matter differently for male and female adolescents.Method: Data come from the National Longitudinal Study of Adolescent to Adult Health (n = 8,534 females, n = 8,102 males). We conduct social network analysis and traditional and cross-classified multilevel models (CCMM) in the full and sex-disaggregated samples.Results: In final CCMM, peer groups, schools, and neighborhoods contributed 10.5%, 10.8%, and 0.4%, respectively, to total variation in adolescent alcohol use. Results do not differ widely by gender.Conclusions: Peer groups and schools emerge as more salient contributing contexts relative to neighborhoods in adolescent alcohol use for males and females. These findings have both methodological and practical implications. Multilevel modeling can model contexts simultaneously to prevent the overestimation of variance in youth alcohol use explained by each context. Primary prevention strategies addressing youth alcohol use should focus on schools and peer networks.

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

AIM

To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques.

METHODS

235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed.

RESULTS

After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained.

CONCLUSION

Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Social network analysis by Turiyam graphs

OBJECTIVE

A single valued neutrosophic set represented the uncertainty of real life situations in terms of membership [Formula: see text], indeterminacy [Formula: see text] and non-membership [Formula: see text] degree. However, this uncertainty cannot be limited to those three degrees; there is also an additional refusal degree. For this issue, the Turiyam set is an appropriate tool, which described the neutrosophic refusal degree of this situation as a liberal [Formula: see text] degree in addition to those three degrees. The graphical representation of this situation is required for knowledge processing. For this purpose, the Turiyam graph was introduced as an extension of the single valued neutrosophic graph. This graph is helpful when the depictions of the vertices or their relationships or both, are considered in terms of membership [Formula: see text], indeterminacy [Formula: see text], non-membership [Formula: see text] and liberal [Formula: see text] degrees. The goal of this paper is to introduce the degree, order and size in the context of Turiyam graphs and examine a social network (SN) with the help of this graph.

RESULTS

In this regard, the degree, order and size in the context of Turiyam graphs are studied. The feasibility of this Turiyam graph is shown by employing its concept in a social network (SN). Finally, the advantage of the Turiyam graph over the existing graph theories is recognized by viewing its better framework.

A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar

BACKGROUND

Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission.

METHODS

We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction.

RESULTS

Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%-79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination.

CONCLUSION

In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities' resiliency to TB introduction decreases as their interconnectivity increases. "Top down" population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.

Using social network analysis in applied psychological research: A tutorial

Social network analysis (SNA) is a highly flexible research method that allows for novel exploration of a wide variety of research phenomena. Evidence from fields as disparate as public health, education, informatics, sociology, and medicine has demonstrated the importance of recognizing the complexity inherent in individuals' connections with others. In this article, we provide a brief conceptual overview of social network theory and methodology, and then demonstrate how to apply SNA to an applied psychological research context studying students embedded in classrooms. We also provide numerous supporting materials on our OSF page, including R code for all analyses, a dataset containing social network data, and a glossary of key terms in social network analysis. We conclude with a set of recommendations for researchers interested in applying SNA to their own contexts and content areas. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

E-cigarette brands and social media influencers on Instagram: a social network analysis

BACKGROUND

Exposure to visual posts featuring e-cigarette products on social media is associated with increased e-cigarette use among US adolescents. Instagram is the largest source of e-cigarette social media marketing, where influencers-for example, bloggers, brand ambassadors-post promotional materials. This study analysed the network of e-cigarette brands and influencers on Instagram, characterising the most central players in e-cigarette social media marketing.

METHODS

We tracked influencers with public profiles on Instagram who posted promotional e-cigarette content in 2020, had over 1000 followers and high user engagement rate (ratio of likes and comments to followers) of 1%-25% per post. By conducting a social network analysis, we identified the most central (highly involved in promotional activities) influencers and e-cigarette brands. The number of the influencers' followers aged 13-17 years old and the age verification practices restricting youth access were also assessed.

RESULTS

There is a highly interconnected network of engaging e-cigarette influencers (n=55) worldwide who collaborated with over 600 e-cigarette brands in 2020. The Asian and US influencers had five to six times more teenage followers compared with the European influencers. 75% of the influencers did not restrict youth access to their promotional content on Instagram. The brands Voopotech, Innokin, Geekvape, Lost Vape, Smok and Vaporesso collaborated with the largest number of influencers (mean n=20).

CONCLUSIONS

It is important to understand associations among influencers and e-cigarette use behaviours, especially youth, to inform effective public health communication and potential policies that could regulate social media marketing sponsored by e-cigarette companies.

Investigating the aftermath of the Türkiye 2023 earthquake: exploring post-disaster uncertainty among Syrian migrants using social network analysis with public health approach

Objectives

On February 6th, 2023, a doublet earthquake struck Türkiye, impacting more than 15 million people including migrants, and resulting in over 50,000 deaths. The Syrian migrants experience multiple uncertainties in their daily lives which are further compounded by multifaceted challenges of the post-disaster environment. Social media was used intensively and with impunity in this environment and thereby provides a window into the explicit and implicit dynamics of daily life after a disaster. We aimed to explore how a post-disaster environment potentially generates new uncertainties or exacerbating pre-existing ones for migrants through social media analysis with an indirect perspective, in the context of 2023-Earthquake in Türkiye and Syrian migrants.

Methods

Social network analysis was used to analyze Twitter-data with the hashtags 'Syrian' and 'earthquake' during a 10-day period beginning on March 22nd, 2023. We calculated network metrics, including degree-values and betweenness-centrality and clustered the network to understand groups. We analyzed a combination of 27 tweets with summative content analysis using a text analysis tool, to identify the most frequently used words. We identified the main points of each tweet and assessed these as possible contributors to post-disaster uncertainty among migrants by using inductive reasoning.

Results

There were 1918 Twitter users, 274 tweets, 124 replies and 1726 mentions. Discussions about Syrian migrants and earthquakes were established across various groups (ngroups(edges > 15) = 16). Certain users had a greater influence on the overall network. The nine most frequently used words were included under uncertainty-related category (nmost_frequently_used_words = 20); 'aid, vote, house, citizen, Afghan, illegal, children, border, and leave'. Nine main points were identified as possible post-disaster uncertainties among migrants.

Conclusion

The post-disaster environment has the potential to exacerbate existing uncertainties, such as being an undocumented migrant, concerns about deportation and housing, being or having a child, inequality of rights between being a citizen and non-citizen, being in minority within minority, political climate of the host nation and access to education or to generate new ones such equitable distribution of aid, which can lead to poor health outcomes. Recognizing the possible post-disaster uncertainties among migrants and addressing probable underlying factors might help to build more resilient and healthy communities.

Measuring the Impact of Bedroom Privacy on Social Networks in a Long-Term Care Facility for Hong Kong Older Adults: A Spatio-Social Network Analysis Approach

This study aims to measure the impact of bedroom privacy on residents' social networks in a long-term care (LTC) facility for older adults. Little is known about how the architectural design of bedrooms affects residents' social networks in compact LTC facilities. Five design factors affecting privacy were examined: bedroom occupancy, visual privacy, visibility, bedroom adjacency, and transitional space. We present a spatio-social network analysis approach to analyse the social network structures of 48 residents. Results show that residents with the highest bedroom privacy had comparatively smaller yet stronger groups of network partners in their own bedrooms. Further, residents who lived along short corridors interacted frequently with non-roommates in one another's bedrooms. In contrast, residents who had the least privacy had relatively diverse network partners, however, with weak social ties. Clustering analyses also identified five distinct social clusters among residents of different bedrooms, ranging from diverse to restricted. Multiple regressions showed that these architectural factors are significantly associated with residents' network structures. The findings have methodological implications for the study of physical environment and social networks which are useful for LTC service providers. We argue that our findings could inform current policies to develop LTC facilities aimed at improving residents' well-being.

The ties that bind: Social network analysis describes the social element of medical workforce recruitment to rural/remote Australia

We present the argument that medical recruitment to a previously under-recruited remote town was effected through what Social Network Analysis (SNA) measures as "brokerage" which operates amidst "structural holes". We proposed that medical graduates being generated by the national Rural Health School movement in Australia were particularly affected by the combined effect of workforce lacks (structural holes) and strong social commitments (brokerage) - all key SNA concepts. We therefore chose SNA to assess whether the characteristics of RCS-related rural recruitment had feature that SNA might be able to identify, as operantly measured using the industry-standard UCINET's suite of statistical and graphical tools. The result was clear. Graphical output from the UCINET editor showed one individual as being central to all recently recruited doctors to one rural town with recruitment issues like all the others. The statistical outputs from UCINET characterised this person as the single point of most connections. The real-world engagements of this central doctor were in accord with the description of brokerage, a core SNA construct, relationship with reported the reason for these new graduates both coming and staying in town. SNA thus proved fruitful in this first quantification of the role of social networks in drawing new medical recruits to particular rural towns. It allowed description at the level of individual actors with a potent influence on recruitment to rural Australia. We propose these measures could be helpful as key performance indicators for the national Rural Clinical School programme that is generating and distributing a large workforce in Australia, which appears from this work to have a strong social basis. This redistribution of medical workforce from urban to rural is needed internationally.

The journey to overdose: Using spatial social network analysis as a novel framework to study geographic discordance in overdose deaths

INTRODUCTION

Drug overdose deaths are often geographically discordant (the community in which the overdose death occurs is different from the community of residence). Thus, in many cases there is a journey to overdose.

METHODS

We applied geospatial analysis to examine characteristics that define journeys to overdoses using Milwaukee, Wisconsin, a diverse and segregated metropolitan area in which 26.72 % of overdose deaths are geographically discordant, as a case study. First, we deployed spatial social network analysis to identify hubs (census tracts that are focal points of geographically discordant overdoses) and authorities (the communities of residence from which journeys to overdose commonly begin) for overdose deaths and characterized them according to key demographics. Second, we used temporal trend analysis to identify communities that were consistent, sporadic, and emergent hotspots for overdose deaths. Third, we identified characteristics that differentiated discordant versus non-discordant overdose deaths.

RESULTS

Authority communities had lower housing stability and were younger, more impoverished, and less educated relative to hubs and county-wide numbers. White communities were more likely to be hubs, while Hispanic communities were more likely to be authorities. Geographically discordant deaths more commonly involved fentanyl, cocaine, and amphetamines and were more likely to be accidental. Non-discordant deaths more commonly involved opioids other than fentanyl or heroin and were more likely to be the result of suicide.

CONCLUSION

This study is the first to examine the journey to overdose and demonstrates that such analysis can be applied in metropolitan areas to better understand and guide community responses.

Identification of Potential Cooperation Relationships Among Scientists

In this article, the phenomenon of scientist cooperation in the scientist cooperation network is studied from the perspectives of information spread and link prediction. By mining the information in the scientist cooperation network, analyzing the cooperation has been generated and discovering potential cooperation opportunities. It helps to build a richer cooperation network with more content. Information spread can reflect the inner laws of network structure formation, and the link prediction method can retain the integrity of network information to the maximum extent. First, the real network is abstracted by analyzing its structure as well as node attributes into a simulated network. Second, the process of information spread in the cooperation network is simulated by improving the traditional SIS model. Some improvements are made to the link prediction algorithm for the impact brought to the network by information spread. Finally, the experimental results in the scientist cooperation network show that the hybrid weighted link prediction algorithm combining node attributes and spread factors can improve the accuracy of link prediction and provide suggestions for scientists to find partners. The comparative experiments on simulated and real networks not only validate the effectiveness of the propagation model in the scientist cooperation network, but also verify the accuracy of the hybrid weighted link prediction algorithm.

Comparing pharmaceutical company payments in the four UK countries: a cross-sectional and social network analysis

OBJECTIVES

To examine the characteristics of pharmaceutical payments to healthcare and patient organisations in the four UK countries. Compare companies spending the most; types of organisations receiving payments and types of payments in the four countries. Measure the extent to which companies target payments at the same recipients in each country and whether it differs depending on the type of recipient.

DESIGN

Cross-sectional comparative and social network analysis.

SETTING

England, Scotland, Wales, Northern Ireland.

PARTICIPANTS

100 donors (pharmaceutical companies) reporting payments to 4229 recipients (healthcare organisations and patient organisations) in 2015.

MAIN OUTCOME MEASURES

For each country: payment totals and distribution; average number of common recipients between companies; share of payments to organisations fulfilling different roles in the health ecosystem and payments for different activities.

RESULTS

Companies prioritised different types of recipient and different types of activity in each country. There were significant differences in the distribution of payments across the four countries, even for similar types of recipients. Recipients in England and Wales received smaller individual payments than in Scotland and Northern Ireland. Overall, targeting shared recipients occurred most frequently in England, but was also common in certain pockets of each country's health ecosystem. We found evidence of reporting errors in Disclosure UK.

CONCLUSIONS

Our findings suggest a strategic approach to payments tailored to countries' policy and decision-making context, indicating there may be specific vulnerabilities to financial conflicts of interest at subnational level. Payment differences between countries may be occurring in other countries, particularly those with decentralised health systems and/or high levels of independence across its decision-making authorities. We call for a single database containing all recipient types, full location details and published with associated descriptive and network statistics.

Risk identification and analysis for the green redevelopment of industrial brownfields: a social network analysis

The green redevelopment of industrial brownfields (GRIB) is an important means to solve the shortage of urban land resources and realize sustainable urban renewal and green development. The identification and analysis of risk factors in GRIB projects are of immense significance for completing construction tasks and ensuring the planned benefits. In this study, work and risk breakdown structures and expert interviews are used to construct the risk network model of a GRIB project, based on three dimensions: process, subject, and system sources. The software package UCINET is used to conduct social network analysis and determine the key risk factors and relationships. The results of this study suggest that the four risk factors with the most brokerage roles and the highest node betweenness centralities are located at the core of the network; the six risk relationships with the highest line betweenness centralities are those with the strongest transmission capacities; the key risk factors are mostly response and stress risks; the main source is the design unit; and the key risk relationships are the influence of the decision-making stage on the design stage and of the design stage on the construction stage. Surpassing the limitations of traditional linear research, this study explains the internal relationship among the risk factors of GRIB projects and identifies the risk factors that play a brokerage role and the risk relationship that plays a conductive role, providing a theoretical basis for introducing social network analysis tools into the risk assessment of such complex construction projects.

Exploring Spatial Correlations of Tourism Ecological Security in China: A Perspective from Social Network Analysis

The imbalance of regional tourism ecological security (TES) is an important barrier to the sustainable development of tourism. Relying on the spatial correlation network to coordinate the regional TES is effective. Taking 31 provinces in China as examples, social network analysis (SNA) and the quadratic assignment procedure (QAP) are used to analyze the spatial network structure of TES and its influencing factors. The results show that (1) the network density and the number of network relationships increased, while the network efficiency remained at approximately 0.7, and the network hierarchy decreased from 0.376 to 0.234. (2) Jiangsu, Guangdong, Shandong, Zhejiang, and Henan were always more central than the average and dominated. Anhui, Shanghai, and Guangxi have much lower centrality degrees than the average, with little effect on other provinces. (3) The TES networks could be divided into four parts: "net spillover", "agent", "bidirectional spillover" and "net benefit". (4) The differences in economic development level, tourism industry dependence, tourism load level, educational attainment, investment in environmental governance, and transportation accessibility all had a negative impact on the TES spatial network, whereas geographic proximity had a positive driving effect. In conclusion, the spatial correlation network of provincial TES in China is increasingly close, but the network structure is loose and hierarchical. The core-edge structure is obvious, and there are significant spatial autocorrelations and spatial spillover effects between provinces. The difference in regional influencing factors has a significant effect on the TES network. This paper presents a new research framework for the spatial correlation of TES and provides a Chinese solution to promote the sustainable development of tourism.

A COVID-19 nosocomial cluster in a university hospital in southern Italy: a social network analysis

INTRODUCTION

Healthcare workers on duty at the hospital are at high risk of COVID-19 infection. However, despite the introduction of risk-lowering practices in the hospital setting, there have been many cases of SARS-COV-2 infection among Health Care Workers. Fast and efficient contact tracing and Sars-CoV-2 PCR-based testing of the close contacts of Health Care Workers with confirmed infections are essential steps to limit nosocomial outbreaks.

METHODS

This cross-sectional study was conducted at Bari Policlinico General University-Hospital (Apulia, Italy) and describes the management of a cluster of SARS-COV-2 infections in three Operative Units. The contact tracing activities and the measures implemented to control the outbreak are described.

RESULTS

Among the 186 Health Care Workers active in the cluster setting, there were 9 (4.8%) confirmed cases, including the index case. Due to the outbreak, three Operative Units were closed to limit virus circulation. Health Care Workers with confirmed infections tested negative after a mean of 28.0±6.6 days (range: 13-37) and none required hospitalization.

CONCLUSIONS

Protection of the health of Health Care Workers during the COVID-19 pandemic should be a public health priority. However, despite recent recommendations and the implementation of protective measures, SARS-COV-2 infections of Health Care Workers remain at a high rate, indicative of the continued high risk of cluster onset in the nosocomial setting.

Social Network Influence on Syphilis Testing for Black Sexual Minority Men in Baltimore, Maryland: A Cross-sectional Social Network Analysis

ABSTRACT

Black sexual minority men (BSMM), including those with HIV, have disproportionate rates of syphilis infection. This study examines the associations of social network characteristics on syphilis testing, given that social network approaches are well established as effective methods to establish health-promoting social norms. We analyzed baseline data from a sexual health behavioral intervention. Using multivariable logistic regression, we modeled individual and social network characteristics on syphilis testing. Of the 256 participants, 37% tested for syphilis in the past year. In the adjusted model controlling for individual factors, odds of syphilis testing increased 89% for each increase in network member participants being encouraged to get a syphilis test (95% confidence interval [1.19-3.00]). Feeling comfortable accompanying a friend for HIV/sexually transmitted infection testing was associated with 2.47 increased odds of syphilis testing. Encouraging and training individuals to discuss sexual health topics with their network members may lead to the establishment of testing in social networks of Black sexual minority men.