Evidence-based practice in traditional persian medicine (TPM): a stakeholder and social network analysis.

BACKGROUND: The utilization of complementary and alternative medicine (CAM) is experiencing a global surge, accompanied by the adoption of national CAM policies in numerous countries. Traditional Persian medicine (TPM) is highly used as CAM in Iran, and the ongoing scientific evaluation of its interventions and the implementation of evidence-based medicine (EBM) encounters various barriers. Therefore, comprehending the characteristics and interactions of stakeholders is pivotal in advancing EBM within TPM policies. In this study, we utilized both classical stakeholder analysis and social network analysis to identify key stakeholders and potential communication patterns, thereby promoting EBM in TPM policy-making. METHODS: A cross-sectional nationwide stakeholder analysis was conducted in 2023 using snowball sampling. The interviews were carried out using a customized version of the six building blocks of health. Data were collected through semi-structured interviews. Stakeholders were assessed based on five factors (power, interest, influence, position, and competency). The connections and structure of the network were analyzed using degree, betweenness, closeness centrality, and modularity index to detect clusters of smaller networks. RESULTS: Among twenty-three identified stakeholders, the Ministry of Health and Medical Education (MOHME) and the Public were the most powerful and influential. The Iranian Academy of Medical Sciences was the most competent stakeholder. Social network analysis revealed a low density of connections among stakeholders. Pharmaceutical companies were identified as key connectors in the network, while the Public, supreme governmental bodies, and guilds acted as gatekeepers or brokers. The MOHME and Maraji were found to be high-ranking stakeholders based on four different centrality measures. CONCLUSION: This study identifies powerful stakeholders in the network and emphasizes the need to engage uninterested yet significant stakeholders. Recommendations include improving competence through education, strengthening international relations, and fostering stronger relationships. Engaging key connectors and gatekeepers is essential for bridging gaps in the network.

Exploring stroke discourse on Twitter through content and network analysis among Indian users.

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

The structure and function of antagonistic ties in village social networks.

Negative or antagonistic relationships are common in human social networks, but they are less often studied than positive or friendly relationships. The existence of a capacity to have and to track antagonistic ties raises the possibility that they may serve a useful function in human groups. Here, we analyze empirical data gathered from 24,770 and 22,513 individuals in 176 rural villages in Honduras in two survey waves 2.5 y apart in order to evaluate the possible relevance of antagonistic relationships for broader network phenomena. We find that the small-world effect is more significant in a positive world with negative ties compared to an otherwise similar hypothetical positive world without them. Additionally, we observe that nodes with more negative ties tend to be located near network bridges, with lower clustering coefficients, higher betweenness centralities, and shorter average distances to other nodes in the network. Positive connections tend to have a more localized distribution, while negative connections are more globally dispersed within the networks. Analysis of the possible impact of such negative ties on dynamic processes reveals that, remarkably, negative connections can facilitate the dissemination of information (including novel information experimentally introduced into these villages) to the same degree as positive connections, and that they can also play a role in mitigating idea polarization within village networks. Antagonistic ties hold considerable importance in shaping the structure and function of social networks.

Governance of the wildlife trade and the prevention of emerging zoonoses: a mixed methods network analysis of transnational organisations, silos, and power dynamics.

INTRODUCTION: The wildlife trade is an important arena for intervention in the prevention of emerging zoonoses, and leading organisations have advocated for more collaborative, multi-sectoral approaches to governance in this area. The aim of this study is to characterise the structure and function of the network of transnational organisations that interact around the governance of wildlife trade for the prevention of emerging zoonoses, and to assess these network characteristics in terms of how they might support or undermine progress on these issues. METHODS: This study used a mixed methods social network analysis of transnational organisations. Data were collected between May 2021 and September 2022. Participants were representatives of transnational organisations involved in the governance of wildlife trade and the prevention of emerging zoonoses. An initial seed sample of participants was purposively recruited through professional networks, and snowball sampling was used to identify additional participants. Quantitative data were collected through an online network survey. Measures of centrality (degree, closeness, and betweenness) were calculated and the network's largest clique was identified and characterised. To understand the extent to which organisations were connected across sectors, homophily by sector was assessed using exponential random graph modelling. Qualitative data were collected through semi-structured interviews. The findings from the quantitative analysis informed the focus of the qualitative analysis. Qualitative data were explored using thematic analysis. RESULTS: Thirty-seven participants completed the network survey and 17 key informants participated in semi-structured interviews. A total of 69 organisations were identified as belonging to this network. Organisations spanned the animal, human, and environmental health sectors, among others including trade, food and agriculture, and crime. Organisation types included inter-governmental organisations, non-governmental organisations, treaty secretariats, research institutions, and network organisations. Participants emphasised the highly inter-sectoral nature of this topic and the importance of inter-sectoral work, and connections were present across existing sectors. However, there were many barriers to effective interaction, particularly conflicting goals and agendas. Power dynamics also shaped relationships between actors, with the human health sector seen as better resourced and more influential, despite having historically lower engagement than the environmental and animal health sectors around the wildlife trade and its role in emerging zoonoses. CONCLUSION: The network of transnational organisations focused on the governance of wildlife trade and the prevention of emerging zoonoses is highly multi-sectoral, but despite progress catalysed by the COVID-19 pandemic, barriers still exist for inter-sectoral interaction and coordination. A One Health approach to governance at this level, which has gained traction throughout the COVID-19 pandemic, was shared as a promising mechanism to support a balancing of roles and agendas in this space. However, this must involve agreement around equity, priorities, and clear goal setting to support effective action.

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

BACKGROUND: Project ECHO® networks at Children's Health Queensland Hospital and Health Service (CHQHHS) are communities of practice designed to mitigate services and systems fragmentation by building collaborative partnerships addressing priority child and youth health needs. Aboriginal and Torres Strait Islander people experience the negative impacts of fragmentation in addition to historical challenges of absent or culturally inappropriate health services. Access to culturally safe and responsive services can be improved by engaging Aboriginal and Torres Strait Islander Health Workers and similar roles in an online community of practice, supporting the integration of cultural and clinical knowledge and self-determination of Aboriginal and Torres Strait Islander consumers in decisions affecting their health. Analysing professional support networks and knowledge sharing patterns helps identify enablers and barriers to partnerships. Using social network research, the multilevel network inclusive of ECHO network members and their colleagues was studied to identify interdisciplinary and cross-sector advice exchange patterns, explore the position of cultural brokers and identify common relational tendencies. METHODS: Social network theories and methods informed the collection of network data and analysis of advice-seeking relationships among ECHO network members and their nominees. Registered members from two ECHO networks were invited to complete the Qualtrics survey. Networks analysed comprised 398 professionals from mainstream health, Aboriginal and Torres Strait Islander Community Controlled Health Organisation, education, disability and child safety service settings. RESULTS: Brokers were well represented, both those who hold knowledge brokerage positions as well as cultural brokers who incorporate clinical and cultural knowledge enabling holistic care for Aboriginal and Torres Strait Islander patients (38 individuals, 17% of network). Professionals who occupy brokerage positions outside the ECHO network tend to be more connected with co-members within the network. CONCLUSIONS: This study is the first application of contemporary social network theories and methods to investigate an ECHO network. The findings highlight the connectivity afforded by brokers, enabling the coordination and collaboration necessary for effective care integration. Inclusion of cultural brokers in an ECHO network provides sustained peer group support while also cultivating relationships that facilitate the integration of cultural and clinical knowledge.

A social network analysis approach to assess COVID19-related disruption to substance use treatment and informal social interactions among people who use drugs in Scotland.

AIMS: To assess the extent of Coronavirus-related disruption to health and social care treatment and social interactions among people with lived or living experience of substance use in Scotland, and explore potential reasons for variations in disruption. DESIGN: Cross sectional mixed methods interview, incorporating a social network 'egonet interview' approach asking about whether participants had interactions with a range of substance use, health, social care or third sector organisations, or informal social interactions. SETTING: Five Alcohol and Drug Partnership Areas in Scotland. PARTICIPANTS: 57 (42% women) participants were involved in the study, on average 42 years old. MEASUREMENTS: Five-point Likert scale reporting whether interactions with a range of services and people had gotten much better, better, no different (or no change), worse, or much worse since COVID19 and lockdown. Ratings were nested within participants (Individuals provided multiple ratings) and some ratings were also nested within treatment service (services received multiple ratings). The nested structure was accounted for using cross classified ordinal logistic multilevel models. FINDINGS: While the overall average suggested only a slight negative change in interactions (mean rating 2.93), there were substantial variations according to type of interaction, and between individuals. Reported change was more often negative for mental health services (Adjusted OR = 0.93 95% CI 0.17,0.90), and positive for pharmacies (3.03 95% CI 1.36, 5.93). The models found between-participant variation of around 10%, and negligible between-service variation of around 1% in ratings. Ratings didn't vary by individual age or gender but there was variation between areas. CONCLUSIONS: Substance use treatment service adaptations due to COVID19 lockdown led to both positive and negative service user experiences. Social network methods provide an effective way to describe complex system-wide interaction patterns, and to measure variations at the individual, service, and area level.

Why do users continue to contribute to darknet Child Sexual Abuse Material forums? Examining social exchange, social capital, and social learning explanations using digital forensic artifacts.

BACKGROUND: The darknet hosts an increasing number of hidden services dedicated to the distribution of child sexual abuse material (CSAM). Given that by contributing CSAM to the forum members subject themselves to criminal prosecution, questions regarding the motivation for members contributing to darknet CSAM forums arise. OBJECTIVE: Building on insights gained from research into clearnet communities, here we examine the extent to which social incentives generated by the online CSAM community may explain members' posting behavior on darknet CSAM forums. PARTICIPANTS AND SETTING: We analyze digital forensic artifacts on the online behavior of members of a darknet CSAM forum that was shut down by law enforcement agencies in July 2015. METHODS: We apply group-based trajectory modelling (GBTM), social network analysis, and mixed-effect survival models. RESULTS: Applying GBTM three posting trajectories can be distinguished. Social network analyses finds the reply network to be more centralized than predicted by chance. Mixed-effect survival models show positive associations between the length of members' first post and the time since members' first registration on the forum and subsequent posting. Contrarily, the number of replies received appears to mitigate subsequent posting. CONCLUSIONS: Findings show posting activity on the forum to be concentrated in a minority of forum members who show posting trajectories that are both frequent and persistent. Results further suggest persistence in posting is motivated by social identity and, to a lesser extent, differential association processes.

The Role of Scientific Research in Human Papillomavirus Vaccine Discussions on Twitter: Social Network Analysis.

BACKGROUND: Attitudes toward the human papillomavirus (HPV) vaccine and accuracy of information shared about this topic in web-based settings vary widely. As real-time, global exposure to web-based discourse about HPV immunization shapes the attitudes of people toward vaccination, the spread of misinformation and misrepresentation of scientific knowledge contribute to vaccine hesitancy. OBJECTIVE: In this study, we aimed to better understand the type and quality of scientific research shared on Twitter (recently rebranded as X) by vaccine-hesitant and vaccine-confident communities. METHODS: To analyze the use of scientific research on social media, we collected tweets and retweets using a list of keywords associated with HPV and HPV vaccines using the Academic Research Product Track application programming interface from January 2019 to May 2021. From this data set, we identified tweets referring to or sharing scientific literature through a Boolean search for any tweets with embedded links, hashtags, or keywords associated with scientific papers. First, we used social network analysis to build a retweet or reply network to identify the clusters of users belonging to either the vaccine-confident or vaccine-hesitant communities. Second, we thematically assessed all shared papers based on typology of evidence. Finally, we compared the quality of research evidence and bibliometrics between the shared papers in the vaccine-confident and vaccine-hesitant communities. RESULTS: We extracted 250 unique scientific papers (including peer-reviewed papers, preprints, and gray literature) from approximately 1 million English-language tweets. Social network maps were generated for the vaccine-confident and vaccine-hesitant communities sharing scientific research on Twitter. Vaccine-hesitant communities share fewer scientific papers; yet, these are more broadly disseminated despite being published in less prestigious journals compared to those shared by the vaccine-confident community. CONCLUSIONS: Vaccine-hesitant communities have adopted communication tools traditionally wielded by health promotion communities. Vaccine-confident communities would benefit from a more cohesive communication strategy to communicate their messages more widely and effectively.

Quality in care homes: How wearable devices and social network analysis might help.

Social network analysis can support quality improvement in care homes but traditional approaches to social network analysis are not always feasible in care homes. Recalling contacts and movements in a home is difficult for residents and staff and documentary and other sources of individual contacts can be unreliable. Bluetooth enabled wearable devices are a potential means of generating reliable, trustworthy, social network data in care home communities. In this paper, we explore the empirical, theoretical and real-world potential and difficulties in using Bluetooth enabled wearables with residents and staff in care homes for quality improvement. We demonstrate, for the first time, that a relatively simple system built around the Internet of Things, Bluetooth enabled wearables for residents and staff and passive location devices (the CONTACT intervention) can capture social networks and data in homes, enabling social network analysis, measures, statistics and visualisations. Unexpected variations in social network measures and patterns are surfaced, alongside "uncomfortable" information concerning staff time spent with residents. We show how technology might also help identify those most in need of social contact in a home. The possibilities of technology-enabled social network analysis must be balanced against the implementation-related challenges associated with introducing innovations in complex social systems such as care homes. Behavioural challenges notwithstanding, we argue that armed with social network information, care home staff could better tailor, plan and evaluate the effects of quality improvement with the sub-communities that make up a care home community.

Use of social network analysis in health research: a scoping review protocol.

INTRODUCTION: Social networks can affect health beliefs, behaviours and outcomes through various mechanisms, including social support, social influence and information diffusion. Social network analysis (SNA), an approach which emerged from the relational perspective in social theory, has been increasingly used in health research. This paper outlines the protocol for a scoping review of literature that uses social network analytical tools to examine the effects of social connections on individual non-communicable disease and health outcomes. METHODS AND ANALYSIS: This scoping review will be guided by Arksey and O'Malley's framework for conducting scoping reviews. A search of the electronic databases, Ovid Medline, PsycINFO, EMBASE and CINAHL, will be conducted in April 2024 using terms related to SNA. Two reviewers will independently assess the titles and abstracts, then the full text, of identified studies to determine whether they meet inclusion criteria. Studies that use SNA as a tool to examine the effects of social networks on individual physical health, mental health, well-being, health behaviours, healthcare utilisation, or health-related engagement, knowledge, or trust will be included. Studies examining communicable disease prevention, transmission or outcomes will be excluded. Two reviewers will extract data from the included studies. Data will be presented in tables and figures, along with a narrative synthesis. ETHICS AND DISSEMINATION: This scoping review will synthesise data from articles published in peer-reviewed journals. The results of this review will map the ways in which SNA has been used in non-communicable disease health research. It will identify areas of health research where SNA has been heavily used and where future systematic reviews may be needed, as well as areas of opportunity where SNA remains a lesser-used method in exploring the relationship between social connections and health outcomes.

Exploration and future trends on spatial correlation of green innovation efficiency in strategic emerging industries under the digital economy: A social network analysis.

With digital technological change and the increasing frequency of interregional innovation links, the spatial correlation and diversity of strategic emerging industries' green innovation efficiency (SEI-GIE) need to be explored in depth. This paper innovatively constructs the SEI-GIE input-output index system under digital economy. The proposed grey model FINGBM(1,1) with ω-order accumulation and weighted initial value optimization realizes effective prediction of 7 input-output indicators of 30 provinces in China from 2021 to 2025. Super-SBM-DEA, gravity model, and social network analysis are applied to explore spatial network structure's dynamic process of SEI-GIE from 12th to 14th Five-Year-Plan period (2011-2025). Empirical results show that (1) Under the effect of digital economy, the SEI-GIE in China generally shows a U-shaped fluctuation trend, in which the growth trend in the central region is obvious, and the western region shows significant fluctuations. (2) The spatial correlation network of SEI-GIE presents a complex and stable center-periphery circle. Particularly, the overall increase in network efficiency highlights the strong small-world characteristics. (3) Beijing, Shanghai, Zhejiang and Jiangsu have always been in the leading core position, with strong influence and control; And Tianjin's core position in the network will decline. Additionally, Guangxi and Chongqing have great potential, but Guangdong needs to strengthen its radiation effect. (4) Block model shows that plate-I (Beijing, Tianjin) receive spatial spillovers from others, while plates-III,IV have significant spillover effects. This study provides theoretical reference for policymakers from a network perspective to promote development of China's SEI-GIE.

Social network analysis of a decade-long collaborative innovation network between hospitals and the biomedical industry in China.

Collaborative innovation between hospitals and biomedical enterprises is crucial for ensuring breakthroughs in their development. This study explores the structural characteristics and examines the main roles of associated key actors of collaborative innovation between hospitals and biomedical enterprises in China. Using the jointly owned patent data within the country's healthcare industry, a decade-long collaborative innovation network between hospitals and biomedical enterprises in China was established and analyzed through social network analysis. The results revealed that the overall levels of collaborative innovation network density, collaborative frequency, and network connectivity were significantly low, especially in less-developed regions. In terms of actors with higher degree centrality, hospitals accounted for the majority, whereas a biomedical enterprise in Shenzhen had the highest degree centrality. Organizations in underdeveloped and northwest regions and small players were more likely to implement collaborative innovation. In conclusion, a collaborative innovation network between hospitals and biomedical enterprises in China demonstrated high dispersion and poor development levels. Stimulating organizations' initiatives for collaborative innovation may enhance quality and quantity of such innovation. Policy support and economic investments, strategic collaborative help, and resource and partnership optimization, especially for small players and in less-developed and northwest regions, should be encouraged to enhance collaborative innovation between hospitals and the biomedical industry in China and other similar countries or regions.

Navigating agricultural nonpoint source pollution governance: A social network analysis of best management practices in central Pennsylvania.

The Chesapeake Bay watershed is representative of governance challenges relating to agricultural nonpoint source pollution and, more generally, of sustainable resources governance in complex multi-actor settings. We assess information flows around Best Management Practices (BMPs) undertaken by dairy farmers in central Pennsylvania, a subregion of the watershed. We apply a mixed-method approach, combining Social Network Analysis, the analysis of BMP-messaging (i.e. information source, flow, and their influences), and qualitative content analysis of stakeholders' interviews. Key strategic actors were identified through network centrality measures such as degree of node, betweenness centrality, and clustering coefficient. The perceived influence/credibility (by farmers) of BMP-messages and their source, allowed for the identification of strategic entry points for BMP-messages diffusion. Finally, the inductive coding process of stakeholders' interviews revealed major hindrances and opportunities for BMPs adoption. We demonstrate how improved targeting of policy interventions for BMPs uptake may be achieved, by better distributing entry-points across stakeholders. Our results reveal governance gaps and opportunities, on which we draw to provide insights for better tailored policy interventions. We propose strategies to optimize the coverage of policy mixes and the dissemination of BMP-messages by building on network diversity and actors' complementarities, and by targeting intervention towards specific BMPs and actors. We suggest that (i) conservation incentives could target supply chain actors as conservation intermediaries; (ii) compliance-control of manure management planning could be conducted by accredited private certifiers; (iii) policy should focus on incentivizing inter-farmers interaction (e.g. farmers' mobility, training, knowledge-exchange, and engagement in multi-stakeholders collaboration) via financial or non-pecuniary compensation; (iv) collective incentives could help better coordinate conservation efforts at the landscape or (sub-)watershed scale; (v) all relevant stakeholders (including farmers) should be concerted and included in the discussion, proposition, co-design and decision process of policy, in order to take their respective interests and responsibilities into account.

An empirical study on social network analysis for small residential communities in Gangwon State, South Korea.

Social Network Analysis (SNA) provides a dynamic framework for examining interactions and connections within networks, elucidating how these relationships impact behaviors and outcomes. This study targeted small residential communities in Gangwon State, South Korea, to explore network formation theories and derive strategies for enhancing health promotion services in rural communities. Conducted in 12 small residential areas, the survey led to a network categorization model distinguishing networks as formal, informal, or non-existent. Key findings demonstrated that demographic and socio-economic factors, specifically age, income, living environment, leisure activities, and education level, significantly influence network formation. Importantly, age, environmental conditions, satisfaction with public transportation, and walking frequency were closely associated with the evolution of formal networks. These results highlight the importance of early community network assessments, which must consider distinct network traits to develop effective health promotion models. Utilizing SNA early in the assessment process can improve understanding of network dynamics and optimize the effectiveness of health interventions.

Social network analysis for medical narcotics in South Korea: focusing on patients and healthcare organizations.

BACKGROUND: Medical narcotics must be administered under medical supervision because of their potential for misuse and abuse, leading to more dangerous and addictive substances. The control of medical narcotics requires close monitoring to ensure that they remain safe and effective. This study proposes a methodology that can effectively identify the overprescription of medical narcotics in hospitals and patients. METHODS: Social network analysis (SNA) was applied to prescription networks for medical narcotics. Prescription data were obtained from the Narcotics Information Management System in South Korea, which contains all data on narcotic usage nationwide. Two-mode networks comprising hospitals and patients were constructed based on prescription data from 2019 to 2021 for the three most significant narcotics: appetite suppressants, zolpidem, and propofol. Two-mode networks were then converted into one-mode networks for hospitals. Network structures and characteristics were analyzed to identify hospitals suspected of overprescribing. RESULTS: The SNA identified hospitals that overprescribed medical narcotics. Patients suspected of experiencing narcotic addiction seek treatment in such hospitals. The structure of the network was different for the three narcotics. While appetite suppressants and propofol networks had a more centralized structure, zolpidem networks showed a less centralized but more fragmented structure. During the analysis, two types of hospitals caught our attention: one with a high degree, meaning that potential abusers have frequently visited the hospital, and the other with a high weighted degree, meaning that the hospital may overprescribe. For appetite suppressants, these two types of hospitals matched 84.6%, compared with 30.0% for propofol. In all three narcotics, clinics accounted for the largest share of the network. Patients using appetite suppressants were most likely to visit multiple locations, whereas those using zolpidem and propofol tended to form communities around their neighborhoods. CONCLUSIONS: The significance of this study lies in its analysis of nationwide narcotic use reports and the differences observed across different types of narcotics. The social network structure between hospitals and patients varies depending on the composition of the medical narcotics. Therefore, these characteristics should be considered when controlling medication with narcotics. The results of this study provide guidelines for controlling narcotic use in other countries.

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

Background: The integration of disparate emergency resources and the improvement of emergency response teamwork are the underlying trends and shared requirements for building resilience in an era of multiple global public health crises. Objective: This study investigated the emergency response with emergency collaboration networks of each functional module and the overall Joint Epidemic Prevention and Control Mechanism (JPCM) network in China's COVID outbreak prevention and control. Methods: The study employed a scholarly framework of "the integration of JPCM coordination and emergency collaborative modularization" to explore the attributes of JPCM using social network analysis. The data were obtained from administrative records from JPCM's official website, spanning January 2020 to December 2022. Results: The study examined the JPCM coordination and found several functional working modules of JPCM, such as Interrupt Spread, Manage Supply, Medical Rescue, Restore Work and Production, and Implement Responsibility modules. The network structure indicators showed that the Manage Supply module had the most extensive network connectivity, the shortest communication distance, and the most consistent collaboration. The E-I index of the overall JPCM network and the Manage Supply network were - 0.192 and - 0.452, respectively (at p < 0.001 and p < 0.05), indicating more internal relationships than external relationships. The E-I index of the Medical Rescue and Implement Responsibility collaboration networks were 0.122 and 0.147, respectively (at p < 0.001 and p < 0.05), indicating more external relationships than internal relationships. The QAP regression analysis showed that the most vital driver on the overall JPCM network was the Interrupt Spread module, followed by the Implement Responsibility and Medical Rescue modules. Discussion: The Interrupt Spread module initiated emergency coordination with most departments and agencies. The Manage Supply module ensured the flow of medical supplies and survival essentials, while the Medical Rescue module addressed the core aspects of the health emergency response. The Restore Work and Production module repaired the halt in production and livelihoods caused by the outbreak, strengthening and developing emergency coordination and roles across emergency organizations. The Implement Responsibility module provided more heterogeneous emergency response resources for the overall JPCM coordination, complementing the COVID cross-organizational emergency response coordination. Conclusion: The study on the JPCM case in China improves public health emergency management and aids informed decision-making.

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

BACKGROUND: Interaction between researchers and policymakers is an essential factor to facilitate the evidence-informed policymaking. One of the effective ways to establish this relationship and promote evidence-informed policymaking is to employ people or organizations that can play the role of knowledge brokers. This study aims to analyze the communication network and interactions between researchers and policymakers in Iran's health sector and identify key people serving as academic knowledge brokers. METHODS: This study was a survey research. Using a census approach, we administered a sociometric survey to faculty members in the health field in top ten Iranian medical universities to construct academic-policymaker network using social network analysis method. Network maps were generated using UCINET and NetDraw software. We used Indegree Centrality, Outdegree Centrality, and Betweenness Centrality indicators to determine knowledge brokers in the network. RESULTS: The drawn network had a total of 188 nodes consisting of 94 university faculty members and 94 policymakers at three national, provincial, and university levels. The network comprised a total of 177 links, with 125 connecting to policymakers and 52 to peers. Of 56 faculty members, we identified four knowledge brokers. Six policymakers were identified as key policymakers in the network, too. CONCLUSIONS: It seems that the flow of knowledge produced by research in the health field in Iran is not accomplished well from the producers of research evidence to the users of knowledge. Therefore, it seems necessary to consider incentive and support mechanisms to strengthen the interaction between researchers and policymakers in Iran's health sector.

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

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

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

BACKGROUND: Nurses play a critical role in providing evidence-based, high-quality care to optimize patient outcomes. Models from implementation science suggest social networks may influence the adoption of evidence-based practices (EBPs). However, few studies have examined this relationship among hospital nurses. Social network analysis (SNA) mathematically evaluates patterns of communication, a critical step in implementation. Exploring hospital nurses' communication networks may provide insight into influences on the adoption of EBPs. AIMS: This study aimed to describe complete communication networks of hospital nurses for practice changes on inpatient units, including upper level nursing administrators. METHODS: This descriptive, exploratory, cross-sectional study used SNA on two inpatient units from one hospital. A sociometric survey was completed by nurses (unit to executive level) regarding communication frequency about practice changes. Network-level density, diameter, average path length, centralization, and arc reciprocity were measured. Attribute data were used to explore subnetworks. RESULTS: Surveys from 148 nurses on two inpatient adult intensive care units (response rates 90% and 98%) revealed high communication frequency. Network measures were similar across the two units and among subnetworks. Analysis identified central (charge nurses and nurse leaders) and peripheral members of the network (new-to-practice nurses). Subnetworks aligned with the weekend and shift worked. LINKING EVIDENCE TO ACTION: Established communication channels, including subnetworks and opinion leaders, should be used to maximize and optimize implementation strategies and facilitate the uptake of EBPs. Future work should employ SNA to measure the impact of communication networks on promoting the uptake of EBP and to improve patient outcomes.

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.