A qualitative social network analysis of decision-making around child marriage in three villages in Bangladesh

Together We Can Improve Outcomes in Kidney Failure: Examining Social Networks in Hemodialysis

Faculty development is increasingly acknowledged as an important aspect of health professions education. Its conceptualization has evolved from an individual skills training activity to contemporary notions that draw on an organizational model. This organizational model recognizes relationships and networks as important mediators of knowledge mobilization. Although such conceptual advancements are critical, we lack empirical evidence and robust insights into how social networks function to shape learning in faculty development. The purpose of this study was to understand how informal professional social networks influence faculty development learning in the health professions. This study used a qualitative social network approach to explore how teaching faculty's relationships influenced their learning about teaching. The study was conducted in 2018 in an undergraduate course at a Canadian medical school. Eleven faculty participants were recruited, and 3 methods of data collection were employed: semistructured interviews, participant-drawn sociograms, and demographic questionnaires. The social networks of faculty participants influenced their learning about teaching in the following 4 dimensions: enabling and mobilizing knowledge acquisition, shaping identity formation, expressing vulnerability, and scaffolding learning. Faculty developers should consider faculty's degree of social embeddedness in their professional social networks, as our study suggests this may influence their learning about teaching. The findings align with recent calls to conceptually reorient faculty development in the health professions as a dynamic social enterprise.

Older adults’ family networks and social networks are important factors that influence life satisfaction, but their transmission mechanisms have not been adequately discussed. The objective of this study was to examine the mechanisms through which family networks and social networks influence the life satisfaction of older adults. We empirically examined the effects and mechanisms of older adults’ family networks and social networks on their life satisfaction using the 2018 China Longitudinal Aging Social Survey with a sample size of 11,418 older adults aged 60 years and older. In the research sample, 6.47% of older adults were subjected to at least one form of mistreatment. The research results indicate that family networks (β = 0.0060, p < 0.05) and social networks (β = 0.0122, p < 0.01) have a significant positive effect on older adults’ life satisfaction. The mechanism-of-action test found that family networks and social networks enhance older adults’ life satisfaction, mainly by reducing the level of physical mistreatment they experience, but these networks cannot improve the life satisfaction of the elderly by reducing their emotional mistreatment. Further research found that community-provided medical escorts, home chores, and meal delivery services can all alleviate the decline in life satisfaction among older adults due to emotional mistreatment. This study deepens our understanding of how older adults’ family and social networks affect their life satisfaction as we examine the mediating role of the physical and emotional mistreatment of older adults and discuss the effects of potential policy interventions.

Family networks and significant relationships with other people are seen as sources of different types of support. Kinship and social networks differ between individuals, as they are shaped by family biographies and the survival of their members. The aim of the paper was to examine the size and the structure of family and social networks among older people in selected European countries, while taking into account differences by sex, age, parental status, and country. Data from the wave 6 of SHARE survey, carried out in 17 European countries, was used. The older people’s family networks were quite large (on average, 7.8 persons), while their social networks were much smaller (on average, 2.6 persons). The family and social networks differed slightly by sex and age group, but significantly between the analysed countries. Although the size and composition of family and social networks differed significantly by parental status, the differences in SN size were much smaller between older parents and people without surviving children. The study contributes to family demography research by estimation the size and the composition of family and social networks among older people in European countries.

Family caregivers of home hospice cancer patients often experience burden and distress, which can be mitigated by perceived social support. However, less attention has been paid to the non-family sources of support within social networks, or to how sources of support may also be sources of stress. We describe support and stress in social networks of hospice family caregivers and identify caregiving characteristics associated with classes identified in our data. We collected demographic and psychosocial self-report data from family caregivers providing in-home hospice care for advanced cancer patients (N = 90). Caregivers also reported perceived support and stress from specific family and non-family relationships. We identified three classes with unique patterns of stress and support within caregivers' support networks using a latent class analysis. Classes include: 1) high support, low stress across family and non-family network members ("supportive"; 53% of caregivers); 2) high support, high stress across family and non-family network ("ambivalent maximizers"; 26%); and 3) high support, high stress across family network only ("family-focused ambivalent"; 21%). Caregivers in the ambivalent maximizer class reported more burden than caregivers in the supportive class (p = .024). This is one of the first studies to systematically explore the role of non-family support, as well as how stress and support co-occur within relationships and across networks. As informal support networks of hospice family caregivers are complex and multifaceted, understanding the patterns of support and stress across various network members is essential to offer services to more effectively manage caregiver burden.

Appetite loss in older adults raises the risk of malnutrition and frailty. The recent emphasis on psychological and social support for appetite loss reveals the importance of robust social networks. Depression is linked to a decline in appetite and social networks. Social networks may influence appetite directly and indirectly through depression. This exploratory cross-sectional study categorizes social networks into family and friend networks to elucidate their direct and indirect effects. The study analyzed 193 community-dwelling older adults (women 78.2%; mean age 77.1±5.3 years) who participated in health-checkup events in two cities in Japan. Appetite was assessed using the Japanese version of the Simplified Nutritional Appetite Questionnaire, and family and friend networks were assessed using the Lubben Social Network Scale-6. Depression was assessed using the Geriatric Depression Scale-15. Based on previous research, we constructed a causal model examining the impacts of family and friend social networks and depression on appetite and calculated the direct and indirect effects through structural equation modeling. The family network had a direct effect on appetite (path coefficient=0.18) and an indirect effect via depression (path coefficient=0.0608). Conversely, the friend network was not directly associated with appetite but had an indirect effect through depression (path coefficient=0.095). The model exhibited a good fit. The mechanism of influence on appetite varied between the networks. To prevent appetite loss, social networks with family and friends should be assessed separately, and tailored support should be provided for each.

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

Social support network, social support, self-efficacy, health-promoting behavior and healthy aging among older adults: A pathway analysis

This paper proposed a framework and an algorithm for identifying communities in dynamic social networks. In order to handle the drawbacks of traditional approaches for social network analysis, we utilize the community similarities and infrequent change of community members combined with community structure optimization to develop a Group-based social community identification model to analyze the change of social interaction network with multiple time steps. According to this model ,we introduced a greed-cut algorithm and depthsearch-first approach and combine them to develop a new algorithm for dynamic social interaction network recognition (called ADSIN). In addition, we conduct experiments on the dataset of Southern Women, the experiment results validate the accuracy and effectiveness of ADSIN. Key words-social network; community recognition; time step; φ

This paper proposed a framework and an algorithm for identifying communities in dynamic social networks. In order to handle the drawbacks of traditional approaches for social network analysis, we utilize the community similarities and infrequent change of community members combined with community structure optimization to develop a Group-based social community identification model to analyze the change of social interaction network with multiple time steps. According to this model ,we introduced a greed-cut algorithm and depth-search-first approach and combine them to develop a new algorithm for dynamic social interaction network recognition (called ADSIN). In addition, we conduct experiments on the dataset of Southern Women, the experiment results validate the accuracy and effectiveness of ADSIN.

Childhood obesity is a pressing global public health concern shaped by genetic, nutritional, lifestyle, and socio-economic factors. This study examines the influence of distinct social networks—family, neighborhood, internet, and social-cultural networks—on childhood obesity. To assess each network's impact, scrutinizing their contributing factors and interactions to inform effective policy development. The study employs a comprehensive literature review, incorporating data from Chinese and English sources. It utilizes the Social Ecological Model (SEM) to analyze the impact of the family network on childhood obesity and examines various factors within the neighborhood environment. The study also investigates the influence of internet social networks, particularly through social media influencer marketing, and explores the impact of social and cultural networks, with a focus on China and the United States. The family network significantly influences childhood obesity, with factors like parental characteristics, age, race, and gender exhibiting associations. Proximity to food outlets and physical activity facilities within the neighborhood plays a key role. Internet social networks can sway children's dietary choices, with influencers promoting unhealthy foods leading to increased consumption. Social and cultural networks in different contexts contribute, influenced by traditional beliefs and migration dynamics. This study underscores the intricate and multifaceted influence of social networks on childhood obesity, highlighting the roles played by each network in shaping children's weight status and dietary choices. Understanding these network dynamics is vital for designing effective prevention and intervention strategies.

Purpose. Health status and social networks are associated with resilience among older adults. Each of these factors may be important to the ability of adults to remain in rural and remote communities as they age. We examined the association of health status and social networks and resilience among older adults dwelling in a rural and remote county in the Western United States. Methods. We selected a random sample of 198 registered voters aged 65 years or older from a frontier Wyoming county. Hierarchical linear regression was used to examine the association of health status as well as social networks and resilience. We also examined health status as a moderator of the relationship between social networks and resilience. Results. Family networks (p = 0.024) and mental health status (p < 0.001) significantly predicted resilience. Mental health status moderated the relationship of family (p = 0.004) and friend (p = 0.021) networks with resilience. Smaller family and friend networks were associated with greater resilience when mental health status was low, but not when it was high. Conclusion. Efforts to increase mental health status may improve resilience among older adults in rural environments, particularly for those with smaller family and friends networks.

Chapter 3 - Privacy-Preserving Social Network Integration, Analysis, and Mining

Background: Social ties within social networks have been shown to influence healthy lifestyles. However, little is known about the association among size of familial social networks, social network dynamics (such as frequency of contact and perceived connectedness), and healthy lifestyle factors in Hispanic/Latino adults. We examined cross-sectional associations of central family social network size, as well as frequency of contact with central family members (children, parents, in-laws) and perceived connectedness to extended family members (uncles, aunts, and other relatives), with individual healthy lifestyle factors. Methods: Data were analyzed from 15,511 self-identified Hispanic/Latino adults ages 18-74 years from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Size of central family, frequency of contact with central family members in past 2 weeks, and perceived connectedness to extended family, were categorized into approximate tertiles based on the distribution of the data. Healthy lifestyle factors included alcohol use (men &lt;30g/day; women &lt;15g/day), not currently smoking, body mass index [BMI] 18.5 to &lt;25.0 kg/m 2 , physical activity in the highest sex-specific 40%, and healthy diet in the highest sex-specific 40%. Survey logistic regression was used to compute odds ratios [OR] and 95% confidence intervals [CI], with models adjusted for age, sex, education, income, Hispanic/Latino background, employment status, religion, church attendance, marital status, acculturation, and language preference. Results: Compared to participants with a central family social network of 0-3 individuals, those with a family social network of 4-5 members were significantly less likely to have a healthy diet (OR: 0.86; 95% CI: 0.74, 0.99) or a healthy BMI (OR: 0.74; 95% CI: 0.65, 0.85). Also, those with 6-11 members were also significantly less likely to have a healthy BMI (OR: 0.58; 95% CI: 0.49, 0.69). Individuals reporting frequent contact with 3-4 and 5-11 family members were less likely to have a healthy BMI (OR: 0.76; 95% CI: 0.66, 0.87; OR: 0.60; 95% CI: 0.51, 0.72; respectively) versus those reporting frequent contact with only 0-2 family members. However, individuals who reported feeling connected to 3-5 extended family members were more likely to have a healthy diet (OR: 1.16; 95% CI: 1.00, 1.36) and healthy BMI (OR: 1.15; 95% CI: 1.00, 1.33), and those who felt connected to 6-7 extended family members were more likely to be non-smokers and to have a healthy BMI (OR: 1.16; 95% CI: 1.00, 1.34; OR: 1.20, 95% CI: 1.20; 95% CI: 1.04, 1.38; respectively) versus those who reported feeling connected to 0-2 extended family members. Conclusions: These findings suggest that social network size and dynamics may play an important role in influencing healthy lifestyle factors among Hispanic/Latino adults. Further, specific influences may differ based on the type of relationship.

Mismatch Negativity, Social Cognition, and Functioning in Schizophrenia Patients