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.

Scaling up One Health: A network analysis in Lao PDR

Background

One Health focuses on sustainable health for humans, animals, and ecosystems. The approach has been well demonstrated, yet most efforts have not been scaled up. Understanding the organisations involved in scaling up processes is critical to translating research into practice. The Lao People's Democratic Republic has successfully implemented One Health projects for multiple decades; however, the organisational network has not been described and scaling up efforts have been limited.

Methods

Data from organisations involved in One Health projects over the past five years were collected by key-informant interview or workshop. The network was investigated using a mixture of quantitative network analysis and qualitative thematic analysis.

Results

The organisational network was quantitatively described as sparse and centralised. Organisations were required to harness pre-existing relationships to maximise scarce resources and make co-ordination and alignment of priorities more efficient. A lack of international organisations in the top 10% of resource sharing metrics suggests a potential disconnect between donors. This was reflected in the challenges faced by national organisations and a feeling of being stretched thin over numerous externally funded projects with donor-driven priorities.

Conclusions

It appears that high-level political support for country ownership of development and aid priorities remains unrealised. Developing network capacity and capability may assist scaling up efforts and build resilience in the network and its core organisations. This may allow for the inclusion of more development, education, environment, and water, sanitation, and hygiene organisations that were perceived to be lacking. Future One Health programmes should focus on practical activities that do not overload staff capacity. There is much for One Health to learn about the art of scaling up and organisations are encouraged to include implementation science in their research to inform future scaling up efforts.

Construction and evolutionary factors of spatial correlation network of China's provincial tourism resource conversion efficiency

Research objective

To explore the spatial correlation network characteristics and formation mechanisms of tourism resource conversion efficiency, and provide reference for the collaborative improvement of tourism resource conversion efficiency at the provincial level in China.

Research methods

Non parametric SBM efficiency measurement method and social network analysis method. Research hypothesis: The spatial network correlation characteristics of tourism resource conversion efficiency are obvious, and regional connections are close.

Research findings

(i) during the research period, the spatial connection strength of China's tourism resource conversion efficiency continued to increase and the spatial network structure of tourism resource conversion efficiency tended to become more complex and significantly more stable. (ii) A spatially linked network with a stable tourism resource conversion efficiency structure formed in China. The number of network relations and density of the network fluctuated and increased, while the network efficiency continued to decrease; however, a strong small-world nature was observed. (iii) The economic development level difference matrix, tourism industry agglomeration difference matrix, human capital difference matrix, and marketization degree difference matrix significantly and positively affected spatial association relationship establishment, while the provincial adjacency matrix significantly and negatively affected such relationships.

Implementation processes of social network interventions for physical activity and sedentary behavior among children and adolescents: a scoping review

BACKGROUND

The characteristics of the implementation process of interventions are essential for bridging the gap between research and practice. This scoping review aims to identify the implementation process of social network interventions (SNI) to address physical activity and sedentary behaviors in children and adolescents.

METHODS

The scoping review was conducted adhering to the established guidelines. The search was carried out in the ERIC, EBSCO, EMBASE, SCOPUS, and Lilacs databases in April 2023. Social network intervention studies in children and adolescents were included, addressing physical activity or sedentary behaviors. Replicability (TIDieR), applicability (PRECIS-2), and generalizability (RE-AIM) were the explored components of the implementation process. Each component was quantitatively and separately analyzed. Then, a qualitative integration was carried out using a narrative method.

RESULTS

Most SNI were theoretically framed on the self-determination theory, used social influence as a social mechanism, and used the individual typology of network intervention. Overall, SNI had strong replicability, tended to be pragmatic, and three RE-AIM domains (reach, adoption (staff), and implementation) showed an acceptable level of the generalizability of findings.

CONCLUSIONS

The analyzed SNI for physical activity and sedentary behaviors in adolescents tended to be reported with high replicability and were conducted pragmatically, i.e., with very similar conditions to real settings. The RE-AIM domains of reach, adoption (staff), and implementation support the generalizability of SNI. Some domains of the principles of implementation strategies of SNI had acceptable external validity (actor, action targets, temporality, dose, and theoretical justification).

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.

The spatial correlation network structure and its formation mechanism of urban high-quality economic development: a comparative analysis from the Yangtze River Economic Belt and the Yellow River Basin in China

In the context of regional integration, it is more than crucial to compare and analyze the spatial correlation network structure and formation mechanism of high-quality economic development in the Yangtze River Economic Belt and the Yellow River Basin urban cities as an attempt to strengthen collaborative work on high-quality economic development in both river basins. The paper measured high-quality economic development of the Yangtze River Economic Belt and the Yellow River Basin from 2010 to 2021. Then, it employed social network analysis and the QAP method to study the network structure's characteristics and formation mechanism. The conclusion of the research illustrates a few points clearly that first, the high-quality economic development of the two rivers presents a complex and multithreaded network structure. Although the network structure is hold at a comparatively stable state, the correlation degree needs improvement. Second, cities such as Chongqing, Wuhan, Hefei, Nanjing, Hangzhou, Shanghai, and Changsha and cities like Zhengzhou, Xi'an, Luoyang, Yulin, Hulunbuir, Ordos, and Nanyang are at the very central as well as central position of the network. The spatial correlation networks of the Yangtze River Economic Belt and the Yellow River Basin can be divided into four plates: "agent plate," "main outflow plate," "bidirectional spillover plate," and "main inflow plate." Third, reverse geographical distance and differences in the digital economy attach great significance to the spatial correlation networks of the two basins. The difference in urbanization level makes significant impacts only on the spatial correlation network of the Yangtze River Economic Belt, while the difference in environmental regulation and material capital accumulation only significantly influences the spatial correlation network of the Yellow River Basin.

Using personal network analysis to understand the interaction between programmes' facilitators and teachers in psychoeducational interventions

In this study we use personal network analysis to examine the social relationships taking place throughout programme implementation. Previous literature on psychosocial intervention has used network analysis techniques to examine: (a) the interaction between participants, (b) the facilitators' link to the intervention target group, (c) the transfer of knowledge between experts and facilitators, and (d) the interaction of facilitators with each other. However, there has been little research on how facilitators connect with other figures in their organisational context, impacting both intervention fidelity and the fit of the programme to the local context. In this study we combine the analysis of personal networks with qualitative interviews with 102 teachers in 72 schools in Barranquilla (Colombia), with whom we describe the implementation of the psychoeducational programmes Pisotón and Metodologías Flexibles. The results show that programme implementation networks not only rely on facilitators but also on the contribution of the schools' director of studies, the coupling with the regular classroom teachers, and occasional collaborations from the rest of the school staff. After conducting a cluster analysis, we detected the existence of two types of personal networks, some based on the functioning of highly cohesive teams and others with a greater level of centralisation around the head of studies. Implementation networks not only enable the implementation of programme activities, but also the integration of the programme into educational organisations. In the discussion we reflect on how the analysis of facilitators' personal networks can be used to improve the process of programme implementation.

Care complexity, perceptions of complexity and preferences for interprofessional collaboration: an analysis of relationships and social networks in paediatrics

BACKGROUND

In the context of increasingly intricate healthcare systems, professionals are compelled to collaborate within dynamically changing interprofessional teams. Moreover, they must adapt these collaborative processes to effectively and efficiently manage the evolving complexity of care needs. It remains unclear how professionals determine care complexity and relate this complexity to their preferences for interprofessional collaboration (IPC). This study investigated the relationships between care complexity, professionals' perceived complexity and IPC preferences, and examined the variation in individual and team characteristics of IPC-practices across different levels of complexity in paediatric care.

METHODS

In an online questionnaire, 123 healthcare professionals working at an academic tertiary children's hospital scored their perceptions of complexity and preferences for IPC. They also selected family and various professions as members of the interprofessional (IP-) team based on thirteen patient cases. We employed conjoint analysis to systematically model the complexity of case descriptions across the five domains of the International Classification of Functioning, Disability and Health (ICF). Additionally, we applied social network analysis to identify important professions, crucial connectors and influential professions in the IP-team, and to describe the cohesiveness of IP-teams.

RESULTS

Modelled case complexity, professionals' perceived complexity and IPC preferences were positively associated. We found large inter-individual variations in the degree of these associations. Social network analysis revealed that the importance and influence of professions was more equally distributed when case complexity increased. Depending on the context and complexity of the case, different professions (e.g. medical doctors, social professionals, extramural professionals) were considered to be more crucial connectors within the IP-team. Furthermore, team cohesion was positively associated with modelled and perceived care complexity.

CONCLUSIONS

In conclusion, our study contributes to the existing knowledge by integrating task-specific insights and broadening the use of conjoint and social network analysis in the context of IPC. The findings substantiate the contingency theory that relates characteristics of IPC to care complexity, offering quantified insights into how IP-teams adapt to situational needs. This understanding of relationships and variations within IPC holds crucial implications for designing targeted interventions in both clinical and health profession education contexts. Consequently, it contributes to advancements in healthcare systems.

The common drivers of children and young people's health and wellbeing across 13 local government areas: a systems view

BACKGROUND

System dynamics approaches, including group model building (GMB) and causal loop diagrams (CLDs), can be used to document complex public health problems from a community perspective. This paper aims to apply Social Network Analysis (SNA) methods to combine multiple CLDs created by local communities into a summary CLD, to identify common drivers of the health and wellbeing of children and young people.

METHODS

Thirteen community CLDs regarding children and young people health and wellbeing were merged into one diagram involving three steps: (1) combining variable names; (2) CLD merging, where multiple CLDs were combined into one CLD with a set of unique variables and connections; (3) paring, where the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was used to generate a cut-point to reduce the number of variables and connections and to rank the overall importance of each variable in the merged CLD.

RESULTS

Combining variable names resulted in 290 variables across the 13 CLDS. A total of 1,042 causal links were identified in the merged CLD. The DEMATEL analysis of the merged CLD identified 23 common variables with a net importance between 1.0 and 4.5 R + C values and 57 causal links. The variables with the highest net importance were 'mental health' and 'social connection & support' classified as high net receivers of influence within the system.

CONCLUSIONS

Combining large CLDs into a simple diagram represents a generalisable model of the drivers of complex health problems.

Modelling social networks for children of parents with severe and enduring mental illness: an evidence based modification to the network episode model

The Network Episode Model (NEM) is a well utilised model conceptualising how social networks, valuable resources which can positively impact wellbeing and functional outcomes, are responsive to the needs of people with physical and mental health difficulties. Children of parents with severe and enduring mental illness (COPMI) are impacted by these illnesses through the intersecting roles of kin relation, informal carer, and dependent. However, it is not clear that social networks effectively respond in kind to the child's episodic need.We draw upon qualitative data to propose a new multi-factorial conceptual model (COPMI-NEM), triangulating parental mental illness, child's developmental stage and social ties to theorise how social networks do respond and adapt to children's needs.The model illustrates how networks are typically flexible and responsive to visible age-related needs, but less reactive to more obscured needs. Successful network navigation and negotiation of support relies on multiple factors including the child's ability to successfully activate social ties and the availability of formal networks. We propose a new theoretically and empirically informed NEM model also available for testing, refinement and validation in other young carer populations exposed to episodic parental health needs. Findings from this study could be used to generate network informed interventions for this and comparable populations.

Can social network analysis contribute to supply chain management? A systematic literature review and bibliometric analysis

Social Network Analysis (SNA) is a modeling technique and analytical approach well-suited for identifying and examining the structural features of supply networks and the patterns of connections between members within the network. This paper aims to present a systematic review and bibliometric analysis by investigating network structural properties and metrics in supply chain management (SCM) research. The approach involved combining a systematic literature review with a bibliometric analysis, forming a two-part methodology to examine 113 articles published between 2008 and 2023 in 62 journals. Our systematic thematic analysis reveals how SCM researchers have applied SNA techniques in terms of the reported node-level and network-level structural metrics, including network configuration description metrics, centrality measures, supply network subgroups, and models of supply network structure and formation. We identify the gaps in the existing body of literature and propose potential directions for future research. By quantitatively analyzing, classifying, and visualizing bibliographic data of previous studies, this paper provides further insights into the application of network structural properties in SCM research. Furthermore, our findings contribute to a deeper understanding of the significance of the supply network's relational structure and configuration. Considering the disruptions to global supply chains caused by the COVID-19 pandemic and the Russo-Ukrainian War, our findings can contribute to a better understanding of strategic supply network design.

Building a communication and support network among quality improvement teams in nursing homes: a longitudinal study of the SCOPE trial

BACKGROUND

We applied a longitudinal network analysis approach to assess the formation of knowledge sharing and collaboration networks among care aide-led quality improvement (QI) teams in Canadian nursing homes participating in the Safer Care for Older Persons (in residential) Environments (SCOPE) trial which aimed to support unregulated front-line staff to lead unit-based quality improvement (QI) teams in nursing homes. We hypothesized that SCOPE's communicative and participatory nature would provide opportunities for peer support, knowledge sharing, and collaboration building among teams.

METHODS

Fourteen QI teams in Alberta (AB) and seventeen QI teams in British Columbia (BC) participated in the study. Communications across nursing homes occurred through a series of 4 collaborative Learning Congresses (training sessions) over a 1-year period. The senior leaders of QI teams participated in two online network surveys about the communication/collaboration between teams in their province, 1 month after the first, and 6 months later, after the fourth Learning Congress. We developed communication and collaboration network maps pertaining to three time points: before SCOPE, at 2 months, and at 9 months.

RESULTS

Over time, teams made significantly more new connections and strengthened existing ones, within and across regions. Geographic proximity and co-membership in organizational chains were important predictors of connectivity before and during SCOPE. Teams whose members were well connected at baseline disproportionately improved connectivity over time. On the other hand, teams that did not have prior opportunities to connect appeared to use SCOPE to build new ties.

CONCLUSIONS

Our findings suggest the importance of network-altering activities to the formation of collaboration networks among QI teams across nursing homes. Active strategies could be used to better connect less connected teams and facilitate collaboration among geographically proximate teams. These findings may inform the development of interventions to leverage existing networks and provide new networking opportunities to develop and sustain organizational improvements.

The social network perspective on power system co-evolution: responses to "double carbon target" realization in China

The introduction of dual carbon targets will significantly impact power system development. Despite this, there is currently limited research on achieving system evolution and transition while ensuring safety, low-carbon output, and efficiency, as well as quantitatively analyzing the resulting changes dual carbon targets will have on the power system. Co-evolution of the power system offers a solution to balance the impact of dual carbon goals and enhance interaction among system entities, thereby facilitating the achievement of these goals. Our study focuses on constructing an evolutionary topological network by analyzing the dynamic evolution rule of power systems. We investigate the co-evolution pattern of power systems by analyzing the relationship between the role of power system agents and their dynamic structures. Furthermore, we analyze the future structural changes of power systems, which can provide theoretical support for achieving dual carbon goals in the power system. Our findings highlight key measures to promote synergistic evolution, including increasing energy storage capabilities, stabilizing renewable energy supply, breaking inter-provincial barriers in electricity transmission, and developing a multi-level intelligent power system. Through link analysis, we discover that future power systems will maintain a mild coordination among agents rather than implementing large-scale realignment and reconfiguration. We posit that overcoming obstacles can be achieved by fostering cohesion between the network and agents through technological innovation and widespread market diffusion to drive co-evolution.

Collaboration and growth in a large research cooperative: A network analytic approach

Research networks encourage team science and facilitate collaboration within and across research teams. While many analyses have examined the output of these collaborative networks (e.g., authorship networks, publications, grant applications), less attention has been paid to the formative phases of these initiatives. This article presents analyses of a whole-network survey of investigators participating in a new research initiative, and examines the development of collaborative ties over the network's first year. In particular, we examine the influence of research center affiliation, seniority, and prior network experience on the number and structure of collaborative ties, including participants' bridging and broker roles. Such analyses can inform the overall management of the project in purposefully promoting new collaboration opportunities, and may ultimately predict the number of collaborative products generated by the network members.

Can the construction of a high-speed rail alleviate haze pollution: an empirical analysis based on social networks and dynamic spatial econometric models

With the increase in haze pollution in Chinese cities, answering the question of whether using a high-speed rail (HSR) as a green and efficient transportation system can alleviate haze pollution in China has become a research hotspot. This study empirically tests the impact of HSR network construction on haze pollution and its spatial spillover effects. (1) The construction of a HSR would have a mitigation effect on haze pollution in node cities and surrounding cities, and the more developed the HSR is, the more significant that this effect would be. (2) Haze pollution persists for a long time, the haze pollution from the previous year may have a positive promoting effect on the haze pollution in the following year. (3) The use of a HSR reduces haze pollution by replacing traditional road transportation and promoting industrial structure upgrading and technological innovation. (4) The inhibitory effect of HSR use on haze pollution varies due to regional differences and variation in city size.

Alliance-building for equity and justice: An inter-organizational perspective

Cultivating alliances between specific sectors or fields with unrealized synergies is a promising strategy for systems change. Social network analysis is a useful tool for assessing whether such alliances build relationships between these previously disparate organizations. Two waves of valued network data were collected from the members (N = 25) of a justice system reform coalition composed of two types of community-based organizations: those involved in restorative justice practice and those involved in grassroots community organizing. Social network homophily analyses were employed to characterize the impact of alliance participation on relationships over time across three domains: working together, doing impactful work together, and sharing a philosophy about justice system reform. Results indicate marked increases in the prevalence of relationships between restorative justice and community organizing organizations across all domains, with the greatest increases occurring in perceptions of shared philosophy. Network structures at each time point differed by relational domain and organization type. Findings suggest that the alliance strengthened relationships between restorative justice and community organizing organizations, particularly in perceptions of shared philosophy, but that the magnitude of these changes varied depending on pre-existing interorganizational relationships and organization type. Findings challenge the idea that close working relationships between diverse members is critical to coalition effectiveness, pointing instead to the value of ambidextrous networks and alignment in members' philosophy and vision.

The Social Dynamics of Fun: Can Fun Youth Bring Peers Together and Positively Influence Their Friends?

There is a critical gap in our understanding of how peer relationships contribute to positive youth development. To address this gap the current study uses longitudinal social network data to examine if fun youth were socially desirable, inclusive of peers, and positive agents of social influence during the transition to adolescence. Participants were 210 students (47% female; Mage = 11.55 years at the outset) from 8 classes in 3 schools in a small Lithuanian city. Each child received a fun score consisting of nominations from classmates as "someone who is fun to be around". Participants also nominated up to five classmates as friends. Fun students received more friendship nominations than their peers and they maintained higher levels of social desirability over time. Fun youth appeared to be inclusive of their peers in that fun youth did not appear to have preferences to befriend only those like themselves. The friends of fun youth were positively influenced such that they were perceived to be more fun over time. The findings suggests that fun youth may promote positive social change within peer groups.

The Phenomenon and Determinants of Healthcare Service Utilization for Older Adults With Multimorbidity in China: An Explanatory, Mixed-Method Study

OBJECTIVES

The objective of this study was to explore the phenomenon and determinants of healthcare service utilization in Chinese older adults with multimorbidity.

METHODS

We adopted a mixed-methods explanatory design from July 2022 to May 2023. The quantitative research was a social network analysis to explore the phenomenon of healthcare service utilization in target participants. The quantitative results were further interpreted as the participant's propensity for healthcare services and the potential for information sharing between healthcare providers through shared patients. Logistic regression was conducted to identify individual determinants for healthcare service utilization. The quantitative research was followed by qualitative interviews with stakeholders to deeply understand the phenomenon of interest from the individual, healthcare system, and societal perspectives.

RESULTS

We recruited 321 participants for the quantitative study. They preferred using medication services from primary healthcare providers, pharmacists at private pharmacies, and hospital specialists, and preferred using other services from hospital specialists. Dense relationships arose from shared patients among healthcare providers across various professions and settings, making it possible to share patient information. Primary healthcare providers were particularly important in the process, as they were closely related to others through patient sharing. Health status was identified through logistic regression and qualitative interviews as an individual determinant for healthcare service utilization. More determinants were explored in qualitative interviews with 30 stakeholders, including trust, resource allocation, healthcare accessibility, medical treatment process, and healthcare awareness.

DISCUSSION

Strategies should be proposed to intervene with patients' nonoptimal propensity toward healthcare services and promote information sharing among healthcare providers.

Tribal Reservation Adolescent Connections Study: A study protocol using mixed methods for examining social networks and associated outcomes among American Indian youth on a Northern Plains reservation

BACKGROUND

Rich cultural and traditional practices make interpersonal relationships vital for American Indian (AI) youth. Social relations and multigenerational networks (i.e., peer, family, kinship, and community) remain a salient fixture of AI culture, survival and thriving in reservation communities. Research in other populations has demonstrated how social networks impact youth risk and resilience, but data are lacking on AI adolescent networks. Intergenerational trauma from settler colonialism leads some AI youth to early initiation of substance use and subsequent substance misuse, along with disproportionately high risk for suicide and vulnerability to witnessing and experiencing violence. Using network data to develop prevention strategies among this population is a promising new avenue of research. In this study protocol paper, we describe the rationale and methodology of an exploratory study to be conducted with American Indian 9th and 10th graders at three schools on a Northern Plains reservation.

METHODS

This mixed methods study will collect quantitative social network surveys (N = 300) and qualitative interviews (n = 30). The study will examine the extent to which existing social network theories and data metrics adequately characterize AI youth networks or how they may need to be expanded for this population. Associations of network characteristics with risk and protective factors for substance use, exposure to violence, and suicide will also be examined.

DISCUSSION

This innovative methodological approach holds promise for informing the development of effective preventive approaches to address co-occurring risks for substance use, violence and suicide among AI adolescents. Understanding processes that impact social networks among AI adolescents can promote culturally resonant social relationships that may support better outcomes for youth.

Influence of physician networks on the implementation of pharmaceutical alternatives to a toxic drug supply in British Columbia

BACKGROUND

Characterizing the diffusion of adopted changes in policy and clinical practice can inform enhanced implementation strategies to ensure prompt uptake in public health emergencies and other rapidly evolving disease areas. A novel guidance document was introduced at the onset of the COVID-19 pandemic in British Columbia (BC), Canada, which supported clinicians to prescribe opioids, stimulants, and benzodiazepines. We aimed to determine the extent to which uptake and discontinuation of an initial attempt at a prescribed safer supply (PSS) program were influenced through networks of prescribers.

METHODS

We executed a retrospective population-based study using linked health administrative data that captured all clinicians who prescribed to at least one client with a substance use disorder from March 27, 2020, to August 31, 2021. Our main exposure was the prescribing patterns of an individuals' peers, defined as the proportion of a prescribers' professional network (based on shared clients), which had previously prescribed PSS, updated monthly. The primary outcome measured whether a clinician had prescribed their initial PSS prescription during a given calendar month. The secondary outcome was the discontinuation of PSS prescribing, defined as an absence for PSS prescriptions for at least 3 months. We estimated logistic regression models using generalized estimated equations on monthly repeated measurements to determine and characterize the extent to which peer networks influenced the initiation and discontinuation of PSS prescribing, controlling for network, clinician, and caseload characteristics. Innovators were defined as individuals initiating PSS prior to May 2020, and early adopters were individuals initiating PSS after.

RESULTS

Among 14,137 prescribers treating clients with substance use disorder, there were 228 innovators of prescribed safer supply and 1062 early adopters through the end of study follow-up, but 653 (50.6%) were no longer prescribing by August 2021. Prescribers with over 20% of peers whom had adopted PSS had a nearly fourfold higher adjusted odds of PSS prescribing themselves (aOR: 3.79, 95% CI: (3.15, 4.56)), compared to those with no connected safer supply prescribers.

CONCLUSIONS

The uptake of PSS in BC was highly dependent on the behavior of prescribers' peer networks. Future implementation strategies to support PSS or other policies would benefit from leveraging networks of prescribers.

Social Dynamics of Advice-Seeking: A Network Analysis of Two Residency Programs

Phenomenon: Residents interact with their peers and supervisors to ask for advice in response to complicated situations occurring during patient care. To provide a deeper understanding of workplace learning, this study explores the structure and dynamics of advice-seeking networks in two residency programs. Approach: We conducted a survey-based social network study. To develop the survey, we conducted focus group discussions and identified three main categories of advice: factual knowledge, clinical reasoning, and procedural skills. We invited a total of 49 emergency medicine and psychiatry residents who had completed at least six months of their training, to nominate their supervisors and peer residents, as their sources of advice, from a roster. Participants identified the number of occasions during the previous month that they turned to each person to seek advice regarding the three broad categories. We calculated the density, centrality, and reciprocity measures for each advice category at each department. Findings: The response rates were 100% (n = 21) and 85.7% (n = 24) in the emergency medicine and psychiatry departments, respectively. The advice network of emergency medicine residents was denser, less hierarchical, and less reciprocated compared to the psychiatry residents' network. In both departments, PGY-1s were the top advice-seekers, who turned to PGY-2s, PGY-3s, and supervisors for advice. The "procedural skills" network had the lowest density in both departments. There was less overlap in the sources of advice for different advice types in the psychiatry department, implying more selectivity of sources. Insights: Complex social structures and dynamics among residents vary by discipline and level of seniority. Program directors can develop tailored educational interventions informed by their departments' specific network patterns to promote a timely and effective advice-seeking culture which in turn, could lead to optimally informed patient care.

The power of partnership: Exploring collaboration dynamics in U.S. transplant research

INTRODUCTION

Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals.

METHODS

4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization.

RESULTS

16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p ​= ​0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p ​= ​0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters.

CONCLUSIONS

Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

The relationship between cryptomarket drug purchase, social networks and adverse drug events: A cross-sectional study

INTRODUCTION

Drug use and trading are typically social activities; however, supply through cryptomarkets can occur without any in-person social contact. People who use drugs alone may be at higher risk of experiencing harms, for example, due to lack of others who may call for emergency assistance. Alternatively, cryptomarkets may be a source of harm reduction information and drugs with better-known content and dose, potentially reducing the risk of adverse events. This study examines relationships between cryptomarket use, drug-using social networks and adverse drug events for MDMA, cocaine and LSD.

METHOD

A subsample of 23,053 respondents from over 70 countries was collected in the 2018 Global Drug Survey. People who reported using MDMA, cocaine or LSD were asked about using cryptomarkets to purchase these drugs; any adverse drug events requiring medical treatment (combining seeking treatment and should have sought treatment but did not); and social networks who they had used the specific drug with. All measures referred to the last 12 months, hereon referred to as 'recent'. Binary logistic regressions examined relationships between cryptomarket use, drug-using social networks, and adverse drug events, controlling for age, gender, and frequency of drug use.

RESULTS

Adverse events from any drug type were low (5.2%) and for each drug; MDMA (3.5%); cocaine (3.3%); and LSD (3.5%). After controlling for covariates, recent cryptomarket use was associated with increased likelihood of having no drug-using network for each drug type. People who recently used cryptomarkets were more likely to report adverse cocaine (AOR = 1.70 (1.22-2.37)) and LSD (AOR = 1.58 (1.12-2.09)) events. For those reporting a network size >1, network characteristics did not differ with recent cryptomarket use; however, those reporting recent cryptomarket use were more likely to report adverse LSD events (AOR = 1.86 (0.99-3.51)).

CONCLUSION

People who reported purchasing drugs from cryptomarkets more commonly reported having no drug-using network, and cryptomarket purchase was associated with reported adverse events. Our results support the notion that cryptomarket use increases drug-related harm, but further disentanglement of multiple complex mechanisms is needed in future research.

Social/Sexual Networks of People Newly Diagnosed with HIV in Ibadan, Nigeria

Young men who have sex with men (YMSM) in Nigeria are ten times more likely to be living with HIV-1 than other young men. Due to stigma and criminalization of same-sex sexual behavior, YMSM sexual networks are likely to overlap with those of the general population, leading to a generalized HIV-1 epidemic. Due to limited research on social/sexual network dynamics related to HIV-1 in Nigeria, our study focused on YMSM and sought to assess the feasibility and acceptability of collecting social and sexual network data in Network Canvas from individuals newly diagnosed with HIV-1 in Ibadan, Nigeria. The Network Canvas software was piloted at three sites in Ibadan, Nigeria to collect social/sexual network data from 151 individuals newly diagnosed with HIV-1. Our study sample included 37.7% YMSM; participants reported a mean of 2.6 social alters and 2.6 sexual alters. From the 151 egos and 634 alters, 85 potential unique individuals (194 total) were identified; 65 egos/alters were collapsed into 25 unique individuals. Our success collecting network data from individuals newly diagnosed with HIV-1 in Ibadan demonstrates clear feasibility and acceptability of the approach and the use of Network Canvas to capture and manage these data.

Balanced influence maximization in social networks based on deep reinforcement learning

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users' historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

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.

Unraveling the driving factors of the plastic waste trade network formation and dynamics

International trade of plastic waste promotes the global plastic circular economy and improves resource efficiency, but exacerbates the ubiquitous plastic pollution. Understanding the drivers behind the evolution of the global plastic waste trade network (GPWTN) is pivotal for developing new international instruments to end plastic pollution and fostering clean solid-waste trade. Employing social network analysis (SNA) and quadratic assignment procedure (QAP) model, this study structures the GPWTN using bilateral trade data, revealing shifts from highly centralized to cross-layered networks and relevant drivers. It is suggested that Malaysia and Turkey has become the new key recipients of the GPWTN, replacing China, accompanied by the launch of new environmental regulations in some countries. Transportation cost is the most critical factor for the formation of the GPWTN, followed by gaps in resource demand, bio-based resource availability, and transportation accessibility. Trading partners in closer proximity, especially those with contiguous borders, are more likely to trade in waste plastics, while coastal countries play an important role in these partnerships. Economies with more abundant biomaterials, higher incomes, and greater environmental burdens are more likely to be exporters, while economies with scarcer resources and more compelling demands are more likely to import plastic waste. Countries involved in the trade in plastic waste, as either importers or exporters, receive varying degrees of economic benefits but bear potential environmental impacts. Therefore, global plastic pollution control and trade prosperity necessitates necessitate coordinated endeavors from nations and intergovernmental bodies for a mutually advantageous denouement.

Bibliometric and social network analysis of a Clinical and Translational Resource awardee: An Oklahoma experience 2014-2021

Background

Social Network Analysis is a method of analyzing coauthorship networks or relationships through graph theory. Institutional Development Award (IDeA) Networks for Clinical and Translational Research (IDeA-CTR) was designed to expand the capability for clinical and translational research to enhance National Institutes of Health funding.

Methods

All publications from a cohort of clinical and translational scientists in Oklahoma were collected through a PubMed search for 2014 through 2021 in October 2022. For this study's bibliometric portion, we pulled the citations from iCite in November of 2022.

Results

There were 2,391 articles published in 1,019 journals. The number of papers published by year increased from 56 in 2014 to 448 in 2021. The network had an average of 6.4 authors per paper, with this increasing by year from 5.3 in 2014 to 6.9 in 2021. The average journal impact factor for the overall network was 7.19, with a range from 0.08 to 202.73. The Oklahoma Shared Clinical and Translational Resources (OSCTR) network is a small world network with relatively weak ties.

Conclusions

This study provides an overview of coauthorship in an IDeA-CTR collaboration. We show the growth and structure of coauthorship in OSCTR, highlighting the importance of understanding and fostering collaboration within research networks.

FMC2 model based perception grading for dark insurgent network analysis

The burgeoning role of social network analysis (SNA) in various fields raises complex challenges, particularly in the analysis of dark and dim networks involved in illicit activities. Existing models like the stochastic block model (SBM), exponential graph model (EGM), and latent space model (LSM) are limited in scope, often only suitable for one-mode networks. This article introduces a novel fuzzy multiple criteria multiple constraint model (FMC2) tailored for community detection in two-mode networks, which are particularly common in dark networks. The proposed method quantitatively determines the relationships between nodes based on a probabilistic measure and uses distance metrics to identify communities within the network. Moreover, the model establishes fuzzy boundaries to differentiate between the most and least influential nodes. We validate the efficacy of FMC2 using the Noordin Terrorist dataset and conduct extensive simulations to evaluate performance metrics. The results demonstrate that FMC2 not only effectively identifies communities but also ranks influential nodes within them, contributing to a nuanced understanding of complex networks. The method promises broad applicability and adaptability, particularly in intelligence and security domains where identifying influential actors within covert networks is critical.

Relational and interactional dynamic network data from Czech lower-secondary school students

This article introduces a network data set on the friendships and group work interactions among a convenience sample of 276 Czech Grade 6 students from twelve classrooms, supported by student-level demographic, literacy, motivation, and classroom communication data. Gathered longitudinally at the beginning and end of the 2021/2022 school year, the data provide a relational insight into the nature and evolution of early adolescents' friendships. Moreover, the data provide a unique relational and temporal insight into the verbal interaction of students during classroom group work. This dataset constitutes a valuable resource for educational researchers interested in studying classroom group work as well as for social network scientists studying dynamic social networks.

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.

How ICT capital affects the spatial correlation of energy consumption-a new perspective based on spatially correlation network

ICT has significantly transformed the traditional energy production and service methods, changed the correlation characteristics of energy consumption network, and contributed to the establishment of cross-regional, open, and synergistic energy ecological networks. In this paper, social network analysis (SNA), dynamic SYS-GMM model, and mediating effects model are employed to deliberate on the mechanism of ICT capital's influence on the spatial correlation of energy consumption from 2000 to 2019. Firstly, this study employs an enhanced gravity model to precisely delineate the spatial correlation network of energy consumption in China, further applies the SNA to analyze the network structural characteristics, and then uses the econometric model to investigate the influence mechanism and heterogeneity of ICT capital on the spatial correlation of energy consumption. The study demonstrates a progressive spatial correlation in energy consumption in China, with eastern provinces emerge as the center of the network, assuming the position of the "dominant player." Conversely, the central provinces act as the "bridge," and western provinces are positioned at the periphery, referred to as the "edge" of the network. ICT capital contributes to improving the energy consumption spatial correlation, mainly by stimulating green technology innovation, promoting industrial structure optimization, accelerating human capital accumulation, and reducing income inequality. As ICT capital expands, the eastern region becomes more preeminent as a network hub for energy consumption, the central region increases its dominance slightly, and the western region's marginal position does not change significantly. Furthermore, the presence of ICT capital significantly enhances the intensity of energy consumption spatial correlation more prominently for low-carbon pilot areas and high Internet level areas. This study guides provinces to fully utilize ICT capital to reach collaborative energy-saving goals, and promotes the breaking down of regional competitive barriers in energy systems to build cooperative energy conservation ecological networks.

Who decided the new energy vehicles policy in China? From the perspective of policy objects and policy makers

The new energy automobile industry is a comprehensive system that contains Exploration and Manufacture, Consumption and Promotion, Infrastructure Construction and Supporting Industries, which coordinate and supplement with each other. Accordingly, from the perspective of policy object, NEVs policies since 1991 to 2022 could be divided into four fields in China. With policy bibliometric analysis and social network analysis, in each field of policies, its policy networks can be drawn, with statistic of policies released separately, in order to comprehensively analyse the features of NEVs policy making. It is found that: (1) The structure of policy system is balanced among four fields of NEVs policies in China, though with a bias towards Consumption & Promotion, Exploration & Manufacture. (2) Policy makers in all four fields of NEVs policies preferred slightly to formulate policies jointly, rather than acting alone. While policies made by sole actors are part of policy system. (3) GOOSC, MIIT and MOT, as sole actor, played more significant roles in industry-wide, supply-side and demand-side of NEVs industry respectively. (4) Policy networks of all four fields started with the "iron four" (MIIT, NDRC, MOF, MOST), ultimately forming two different ways of development, specialization and sociability. (5) In addition to the government departments, social organizations and enterprises also influenced the policy network, at the edge of network. This paper is of positive significance for understanding the current status and characteristics of policy making in different fields of the NEV industry, beneficial to distinguish potential effective ways to impact on NEVs policy system in China.

Investigation of spatial association network features of construction waste in major Chinese urban agglomeration

The illegal dumping of construction waste (CW) poses an increasingly serious environmental pollution problem with the accelerated rate of urbanization. As CW disposal capacity struggles to match municipal needs, some CW is being diverted to higher resource endowment cities rather than recycled. To address this situation, it is necessary to obtain reliable information on the characteristics and evolution of CW generation networks in China. This study combines a modified gravity model with Social Network Analysis (SNA) to analyze the spatial association networks of CW generation in four Chinese urban agglomerations between 2000 and 2020. Results reveal the evolution characteristics of the CW generation network, including increasing density and correlation and decreasing network efficiency. Furthermore, the Quality Assurance Procedure (QAP) indicates that urbanization level and population size are positively correlated with CW generations, whereas distance plays a negative role, but resources are insignificant for network formation. The findings provide insight into current patterns of waste distribution and a theoretical basis for government policy formulation in the future.

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.

The impact of industrial structure adjustment on the spatial industrial linkage of carbon emission: From the perspective of climate change mitigation

Industrial structure adjustment is an effective way to reduce carbon emissions. When adjusting the industrial structure, changes in the input and output of industrial sectors in one region may cause changes in industrial sectors of other regions, resulting in inter-regional industrial linkages. What is the impact of industrial structure adjustment on the linkages, and how does it impact climate change? The modified gravity model combines the influence and inductive coefficients to build the carbon emission industrial spatial linkage network (ISL) using social network analysis (SNA) in this work with the data of China's 2008-2019 provincial carbon emissions. The impact of industrial structure adjustment on carbon emission industrial spatial linkage network and its effect on climate change mitigation are explored with the help of the STIRPAT model and quality assurance procedure (QAP). The results of this study are as follows: (1) The provincial carbon emissions under industrial spatial linkages show significant network characteristics, the network density increased from 0.1437 in 2008 to 0.323 in 2019. (2) There is a significant spatial spillover effect in the industrial spatial linkage network (2019) centered on Shandong and Jiangsu. The provinces can be divided into 4 blocks, which play different roles in the network. (3) The industrial structure upgrading can significantly promote the carbon emission industrial linkage network and mitigate climate change. (4) The industrial structure rationalization can promote the development of the network and mitigate climate change in the early and late development stages of carbon emissions industrial linkage. In addition, it is necessary for the government to pay more attention to the development level of the carbon emission spatial industrial linkage network when implementing industrial structure adjustment in response to climate change.

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.

Constructing within and between hospital physician social networks for modeling physician research participation

BACKGROUND

Physician participation in clinical trials is essential for the progress of modern medicine. However, the demand for physician research partners is outpacing physicians' interest in participating in scientific studies. Understanding the factors that influence physician participation in research is crucial to addressing this gap.

METHODS

In this study, we used a physician's social network, as constructed from patient billing data, to study if the research choices of a physician's immediate peers influence their likelihood to participate in scientific research. We analyzed data from 348 physicians across 40 hospitals. We used logistic regression models to examine the relationship between a physician's participation in clinical trials and the participation of their social network peers, adjusting for age, years of employment, and influences from other hospital facilities.

RESULTS

We found that the likelihood of a physician participating in clinical trials increased dramatically with the proportion of their social network-defined colleagues at their primary hospital who were participating ([Formula: see text] for a 1% increase in the proportion of participating peers, [Formula: see text]). Additionally, physicians who work regularly at multiple facilities were more likely to participate ([Formula: see text], [Formula: see text]) and increasingly so as the extent to which they have social network ties to colleagues at hospitals other than their primary hospital increases ([Formula: see text], [Formula: see text]). These findings suggest an inter-hospital peer participation process.

CONCLUSION

Our study provides evidence that the social structure of a physician's work-life is associated with their decision to participate in scientific research. The results suggest that interventions aimed at increasing physician participation in clinical trials could leverage the social networks of physicians to encourage participation. By identifying factors that influence physician participation in research, we can work towards closing the gap between the demand for physician research partners and the number of physicians willing to participate in scientific studies.

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.

A holistic AI-based approach for pharmacovigilance optimization from patients behavior on social media

In this paper, we propose a holistic AI-based pharmacovigilance optimization approach using patient's social media data. Instead of focusing on the detection and identification of Adverse Drug Events (ADE) in social media posts in single time points, we propose a holistic approach that looks at the evolution of different user behavior indicators in time. We examine various NLP-based indicators such as word frequency, semantic similarity, Adverse Drug Reactions mentions, and sentiment analysis. We introduce a classification approach to identify normal vs. abnormal time periods based on patient comments. This approach, along with user behavior indicators, can optimize the pharmacovigilance process by flagging the need for immediate attention and further investigation. We specifically focus on the Levothyrox® case in France, which sparked media attention due to changes in the medication formula and affected patient behavior on medical forums. For classification, we propose a deep learning architecture called Word Cloud Convolutional Neural Network (WC-CNN), trained on word clouds from patient comments. We evaluate different temporal resolutions and NLP pre-processing techniques, finding that monthly resolution and the proposed indicators can effectively detect new safety signals, with an accuracy of 75%. We have made the code open source, available via github.

Examining the spatial networks of an urban agglomeration through the lens of multi-dimensional element flow: Evidence from western China

The Chengdu-Chongqing urban agglomeration (CCUA), as the only national-level city cluster in southwestern China, serves as a strategic support for the Yangtze River Economic Belt and an important demonstration area for promoting new urbanization in the country. The study of the networked characteristics of the CCUA contributes to a systematic understanding of its spatial connectivity patterns, optimization of spatial structure and layout, and promotion of high-quality regional development. In this study, we constructed models for traffic flow, information flow, migration flow, and composite flow to calculate the strength of connections between cities and the total flow of various elements in the CCUA. ArcGIS spatial visualization tools were used to depict the spatial connectivity patterns of the element flows within the CCUA. Additionally, social network analysis methods, including network density, centrality, and cohesive subgroups analysis, were employed to reveal the spatial network structure characteristics of the CCUA. The findings are as follows: (1) The overall structure of the cities within the CCUA is relatively loose, with significant differences in connectivity strength. It exhibits a west-strong and east-weak pattern, with Chengdu-Chongqing, Chengdu-Deyang, Chengdu-Mianyang, and Chengdu-Meishan occupying the top tier, while Zigong and Ya'an have relatively weak connections with other cities. Chengdu and Chongqing have prominent positions in the CCUA, with Chengdu having a more prominent core position compared to Chongqing, resulting in an overall hierarchical distribution of "1 + 1+7 + 7". (2) The network density of the element flows in the CCUA is relatively low, indicating a generally weak element connectivity. The centrality of cities other than Chengdu and Chongqing is at a moderate to lower level, suggesting a weak overall resource connectivity capacity in the CCUA. (3) Most cities tend to form cohesive subgroups based on geographic proximity, while the cohesive subgroup in Chongqing is still in its early stages of development due to administrative boundaries. The research results quantitatively depict the spatial network structure characteristics of the CCUA, providing theoretical references for its development.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

Analyzing actors' interaction behavior in land transactions in informal settlement settings: A case study of Burayu city, Ethiopia

Informality plays an imperative role in offering housing for households in developing countries when the formal market cannot provide enough to keep up with residents' demands. The actors' interaction, with one another, plays an imperative role in land transactions in informal settlement areas. As informal actors operate outside the formal land transactions their activities and methods of operation are rarely understood. Therefore, based on social network theory, this paper aims to identify and examine informal actors, their functions, interactions, and power relationships in informal settlement areas. To this end, this study employed key informant interviews, focus group discussions, structured questionnaires, and a review of published literature, as well as official documents. In the study area, most residents acquired land through informal mechanisms. Major actors include farmers, local land administrators, speculators, land brokers, residents, government officials, and religious leaders. The study also uncovers that the role of each actor varies from information provision to price fixing. Their roles and interactions are governed by dynamic networks and occasionally overlap functions. Among the network actors, land brokers are considered the most influential and powerful because they possess a high degree of centrality, closeness, betweenness, and eigenvector. They hold a pivotal position in the network and act as a liaison between the network's actors. Therefore, the roles of land brokers, who often actively influence the land transaction process, should be considered in urban land governance and incorporated in policy formulation and implementation.

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.

Intramural sports social networks and implications for college student physical activity, sense of community, and retention

Objective: This study examines associations between social networks developed through intramural sports and physical activity (PA), sense of community, and retention. Participants: College students participating in intramural sports at a large public university in the Southern United States (n = 250). Methods: Students self-reported PA, sense of community, retention, and social networks developed through intramural sports in Fall 2022. Logistic and linear regression determined significant individual and network factors associated with PA, sense of community, and retention. Results: Participants were significantly more likely to meet PA recommendations (Nagelkerke R²=.24) if they were younger, male, and reported more people in their networks were physically active often. Participants reported a greater sense of community (R² =.28) and retention intention (R² =.26) if they reported meeting PA recommendations, feeling closer to network members, and more relationships improving through intramurals. Conclusion: Results underline the importance of creating quality intramural experiences for college students.

Mapping sustainability reporting research with the UN's sustainable development goal

Non-financial reporting (NFR) has become crucial to corporate sustainability strategies as companies demonstrate their commitment to the environmental, social, and governance actions outlined in the United Nations Sustainable Development Goals (SDGs) Agenda 2030. Among the various mandatory NFR initiatives, Sustainability Reporting (SR) has emerged as a widely adopted practice by companies worldwide. A gap that the study addresses is the theoretical perspectives on SR in the context of SDG. Then we conduct a bibliometric and science mapping analysis of research trends on SR and precisely map SR research to SDGs which is also a gap in the current literature. We find an exponential increase in the number of publications and citations on SR, particularly after 2015, which coincides with increased public awareness and scrutiny of the SDGs. At the country level, Australia leads with a total of 13 SDGs, followed by the UK, Spain, and Italy, which each address 12 SDGs. Emerging economies such as Indonesia, Malaysia, and India have also increased their contributions since 2019. A keyword co-occurrence analysis identified three main clusters: stakeholder engagement, corporate governance, and accountability; sustainable development goals and climate change; and sustainability reporting and global reporting initiatives. All three clusters had highly cited publications related to SDG 8 (decent work), SDG 9 (industry innovation), and SDG 12 (responsible consumption). This highlights the interdisciplinary nature of SR and its relevance to multiple SDGs. The study is distinctive in that we utilized social network analysis to examine the SDG network based on SR publications, which also affirmed the centrality of SDG 9 and 12. We utilized the prominence percentile, which indicates the momentum of a particular topic, to identify future topics in SR that align with the SDGs. These include cause-related marketing, environmentally preferable purchasing decisions, environmental management systems, education for sustainability, and green computing.

Analysis of spatial correlation networks of carbon emissions in emerging economies

Studies have shown that energy consumption from economic development leads to an increase in carbon emissions. Emerging economies, as important sources of carbon emissions with high growth potential, play a crucial role in global decarbonisation efforts. However, the spatial pattern and evolution trend of carbon emissions in emerging economies have not been studied in depth. Therefore, this paper uses the improved gravitational model and carbon emission data from 2000 to 2018 to construct a spatial correlation network of carbon emissions in 30 emerging economies around the world, aiming to reveal the spatial characteristics and influencing factors of carbon emissions at the national level. The results show that the spatial network structure of carbon emissions in emerging economies is closely linked, forming a "big network" of interconnection. Amongst them, Argentina, Brazil, Russia, Estonia, etc. are at the centre of the network and play a leading role. Geographical distance, economic development level, population density, and scientific and technological level have a significant impact on the formation of spatial correlation between carbon emissions. Further use of GeoDetector shows that the explanatory power of two-factor interaction on centrality is greater than that of a single factor, indicating that a single economic development cannot well enhance the influence of countries in the carbon emission network, and needs to be combined with factors such as industrial structure and scientific and technological level. These results are helpful to understand the correlation between carbon emissions between countries from the perspective of the whole and part and provide a reference for optimizing the carbon emission network structure in the future.

Mapping analytical hierarchy process research to sustainable development goals: Bibliometric and social network analysis

The drive to achieve the Sustainable Development Goals (SDGs) becomes more urgent as the 2030 deadline draws near, increasing research in various sectors. Nevertheless, studies that systematically map Analytical Hierarchy Process (AHP) publications with the SDGs need to be more conspicuously lacking. Our study adds a new perspective to the field by creatively bridging this knowledge gap using the Elsevier SDG Mapping Initiative. To find research clusters, trends, and themes linked to SDGs and their connection to environmental sustainability, we thoroughly analyzed 29,897 publications from 2012 to 2022. The analysis showed that SDG 15, SDG 7, SDG 12, SDG 13, and SDG 11 were the top five SDGs, with an environmental focus among the 17 SDGs. These top SDGs had many clusters connected to them, illustrating various sustainability-related problems. The study also looked at connections between SDGs, the nations with the highest rates of productivity, the top contributors, and the journals with the highest citation counts. We discovered three separate SDG clusters using co-occurrence network analysis, each representing a different SDG. We discovered relevant SDGs using Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis and centrality indicators like eigenvector and betweenness. This novel method for publishing analysis combines an AHP focus that aligns with the SDGs with social network analysis and centrality metrics. Our research advances knowledge of how the AHP technique can assess initiatives supporting the SDGs. We offer essential insights into prioritizing sustainable development measures by identifying research clusters, trends, and issues related to environmental sustainability. This study highlights the subject's most important SDGs, productive nations, helpful organizations, and significant journals.

Latent Profile Patterns of Network-Level Norms and Associations with Individual-Level Sexual Behaviors: The N2 Cohort Study in Chicago

Individual-level behavior can be influenced by injunctive and descriptive social network norms surrounding that behavior. There is a need to understand how the influence of social norms within an individual's social networks may influence individual-level sexual behavior. We aimed to typologize the network-level norms of sexual behaviors within the social networks of Black sexual and gender minoritized groups (SGM) assigned male at birth. Survey data were collected in Chicago, Illinois, USA, between 2018 and 2019 from Black SGM. A total of 371 participants provided individual-level information about sociodemographic characteristics and HIV vulnerability from sex (i.e., condomless sex, group sex, use of alcohol/drugs to enhance sex) and completed an egocentric network inventory assessing perceptions of their social network members' (alters') injunctive and descriptive norms surrounding sexual behaviors with increased HIV vulnerability. We used Latent Profile Analysis (LPA) to identify network-level norms based on the proportion of alters' approval of the participant engaging in condomless sex, group sex, and use of drugs to enhance sex (i.e., injunctive norms) and alters' engagement in these behaviors (i.e., descriptive norms). We then used binomial regression analyses to examine associations between network-level norm profiles and individual-level HIV vulnerability from sex. The results of our LPA indicated that our sample experienced five distinct latent profiles of network-level norms: (1) low HIV vulnerability network norm, (2) moderately high HIV vulnerability network norm, (3) high HIV vulnerability network norm, (4) condomless sex dominant network norm, and (5) approval of drug use during sex dominant network norm. Condomless anal sex, group sex, and using drugs to enhance sex were positively and significantly associated with higher HIV vulnerability social network norm profiles, relative to low HIV vulnerability norm profiles. To mitigate Black SGM's HIV vulnerability, future HIV risk reduction strategies can consider using network-level intervention approaches such as opinion leaders, segmentation, induction, or alteration, through an intersectionality framework.

Aligning social networks and co-designed visions to foster systemic innovation in the Alps

Mountain regions face substantial challenges and opportunities arising from global change. The capacity of mountain regions for (systemic) innovation will be determinant to the success of system transformations envisioned by social actors of mountain communities. By analysing the social networks of two regions in the Alps and relating them to desired future visions of sustainable regional development, we provide insights about innovative capacities in mountain regions and propose how to strengthen these capacities in order to support regional transformations. In particular, we analyse innovation systems' collaboration networks to evaluate the innovative capacity of communities, co-construct visions with mountain stakeholders, and assess innovation requirements to meet these visions. Accordingly, we propose changes to the collaboration networks for addressing regional innovation system (RIS) capacities to help achieve visions through systemic innovation. Our analysis furthers the understanding on the type and magnitude of regional innovation system changes required to more effectively address desired transformative visions in mountain regions. We close by discussing implications of our approach for the assessment of innovative capacity and the transformation of mountain regions.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s10113-023-02099-y.