Objectives: This study was to examine the association between structural and functional characteristics of social network and self-rated health in middle-aged Korea population. We also explored gender difference in the relationship between social network and health. Methods: Data were collected from individuals aged 40-69 years old participating in the 2005 survey for the Korean Genome & Epidemiology Study. We examined the association between social network, social support, social conflict and self-rated health using multiple logistic regression analysis stratified by gender. Results: The extent and contact frequency of close people, and social participations were associated by not only the positive function but also the negative function of social network. Both the positive and negative functions of social network affected self-rated health. The relationship between the function of social network and health showed a gender difference: only positive function was significantly associated with health in men while only negative function had significant relationship with health in women. Conclusions: Social support and social conflict affected the health in both genders through different ways. The ambivalent effect of social network on health should be explored further.
This study was carried out to investigate the relationship between social support, social network and health behaviors as surveyed by cross-sectional study in 744 rural people aged above 30 of a community dwelling sample of one county for 6 days of July in 2000. Objectives of this study was in order to establish an effective health promotion. The sample was accrued by face to face interview of direct visiting from clustered sampling method. Interview was conducted by trained medical students with the questionnaire consisted of socio-demographic data, health behavior, social support and social network based on previous literature. The summarized results were as follows: 1. There were significant difference in the level of social support and social network by general characteristic variables except occupation and residency type(p〈0.05). 2. There were significant difference in knowledge about hypertension, smoking status, status of physical exercise, diet patterns by social support and social network in spite of variation of social support and social network subconcept(p〈0.05). And there were significant difference in alcohol drinking status, body weight control and diet pattern according to level of social network(p〈0.05). But smoking status by social support and network results opposite direction(p〈0.05). 3. There were no regular or consistent result in the relationship between social support, social network and health behavior. 4. Major predictors for health behavior on the multiple logistic regression that included general characteristic, social support and social network were age, instrumental social support and worry about health. Significant variables of multiple logistic regression for health behavior that included social support(instrumental and emotional) and social network were instrumental social support and social network. These results suggest that only a instrumental element and social network may be associated with health behavior. Inconsistent with prior research in these some item, a positive consistent relationship was not found between social support, social network and health behavior. So the study should be replicated to determined the reliability of our findings.
Objectives: Given the importance of social determinants of health in promoting the health of slum residents, this study was conducted with the aim of identifying the main dimensions and components of these determinants. Methods: This scoping review study was conducted according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A comprehensive search was performed of PubMed, ProQuest, Scopus, and Web of Science for articles conducted from 2010 to the end of 2019. Studies were selected based on inclusion criteria, with a special focus on studies dealing with the social determinants of physical and mental health or illness. Results: Thirty-three articles were selected to extract information on the social determinants of health. After reviewing the articles, 7 main dimensions (housing, socioeconomic status of the family, nutrition, neighborhood characteristics, social support and social capital, occupational factors, and health behaviors) and 87 components were extracted as social determinants of health among slum dwellers. Conclusions: This framework could be used by planners, managers, and policy-makers when making decisions affecting the health of these settlements' residents due to the common characteristics of slums around the world, especially in developing countries.
Objectives: The purpose of this study was to identify factors(perceived health and social support) affecting social capital formation of the elderly. Methods: Samples consisted of 184 people aged over 65 years old in one county of Kyungsangbukdo. Data were analyzed with t-test, ANOVA and regression analysis. Results: First, social support(both emotional support and instrumental support) were significantly different by economic status. Secondly, regression analysis showed that perceived health affected a positive influence on social capital in Model1, which analyzed only perceived health. However, Model2, which analyzed perceived health and social support(emotional support, instrumental support) at the same time, showed that only instrumental support affected social capital formation. Conclusions: It was suggested to develop customized health promotion and job creation are required for social capital formation of the elderly.
Background: Social support contributes directly and indirectly to maintaining physical, mental, and social well-being. The aim of the study was to identify the impact of social support on self-rated health among Korean industrial accident workers. Methods: This study used data from the panel study of workers' compensation insurance (PSWCI). The final subjects were 2,759 workers who responded to a 2018 to 2020 PSWCI. Social support was defined as social contact with friends, neighbors, family, and social participation activities like religious activity, social activity, and club activity. Multivariate logistic regression analysis was performed to investigate causal relationships between social support and self-rated health using a generalized estimating equation model. Results: Proportion of workers' good self-rated health steadily increased (2018: n=1,447, 63.2%; 2019: n=1,542, 66.2%; 2020: n=1,653, 67.3%). Higher levels of social contacts with friend (worse: reference; same: β=0.442) and higher levels of social activity (yes: reference; no: β=-0.173) were especially associated with good self-rated health. Conclusion: This study confirmed social support positively influenced self-rated health among the self-rated health of industrial injured workers. The results of this study suggested that recovery policies that the government served should include programs enhancing social support for improving health among industrial injured workers.
Objectives: The purpose of this research is to measure the level of individual social capital, and to reveal the associations between social capital and self-rated health status and how the administrative section(dong, eup, and myeon) might modify the relationships. Methods: This study used the data from Gyeongsangnam-Do health survey (2008). The study subjects were 6,500 adults randomly sampled from 20 counties. Trained interviewers conducted the interviews in the interviewees' houses using structured questionnaires. The association of social capital with self-rated health was analyzed using hierarchical logistic regression. Results: The proportion of trust and social participation were the highest at eup region and the lowest at myeon, The significant social capital associated with self-rated good health were both social participation and trust in the subgroups of dong. The significant social capital associated with self-rated good health were social participation and trust in the subgroups of eup. The significant social capital associated with self-rated good health was trust in the subgroups of myeon. Conclusions: This study highlights that self-rated good health was associated with social capital measured by social participation and trust, and the direction is different in the administrative section. But, health policy encouraging social capital to improve health should be considered.
Objectives: This study intends to understand the difference of social support levels and the relationship between social support the health status, college adjustment and academic achievement in the college student. Methods: Data were obtained from self-administered questionnaire of 416 college student. We measured the demographic characteristics, social support (tangible support, appraisal support, belonging support, self-esteem support), health status (36-item short-form health survey(SF-36), center for epidemiologic studies-depression(CES-D), perceived stress scale(PSS)), student adaptation to college questionnaire(SACQ), average grades point. Chi-square test, t-test, ANOVA test, pearson correlation analysis were used for analysis factors relation of the social support of the college students. Results: In considering the degree of social support by the demographic characteristics in the college students, the social support was better for the female college students. In considering the relation between social support and health status, the students who get better social support, were good in health depression and perceived stress status. When they got better social support their college adjustment and academic achievement were good. The result was statistically significant. Conclusions: Social support for students has great influence on health, college adjustment and academic achievement of students. Psychological aspects of students should be included in the strategy of social support for students.
This study was aimed to assess the social functions of rural residents and to identify factors related with social function. This study conducted a survey using interview-type questionnaires with the Rand social health battery, and measured social function and the related factors between August 21th and August 23th, at 2001. The study subjects were 546 people (158 males, 388 females) living in Chungju-City. The social functions of the subjects were very weak. 20.7 % of respondents said they had no family in their neighbors and they were not well enough acquainted with neighborhood to visit each other. The 24.2 % of respondents said they had no mend that they felt at ease with and could talk frankly. Social function scores were significantly associated with economic status (p=0.0494), having a vehicle (p=0.0019), daily living activity (p=0.0092) in multiple analysis. However, there was no association with age, education, smoking, alcohol consumption, and chronic disease. Our finding showed that social function was not associated with health behaviors definitely. However, It may be important to change existing social network and social function in order to change health behaviors.
Objectives: Considering the importance of social determinants of health (SDHs) in promoting the health of residents of informal settlements and their diversity, abundance, and breadth, this study aimed to identify, measure, and rank SDHs for health promotion interventions targeting informal settlement residents in a metropolitan area in Iran. Methods: Using a hybrid method, this study was conducted in 3 phases from 2019 to 2020. SDHs were identified by reviewing studies and using the Delphi method. To examine the SDHs among informal settlement residents, a cross-sectional analysis was conducted using researcher-made questionnaires. Multilayer perceptron analysis using an artificial neural network was used to rank the SDHs by priority. Results: Of the 96 determinants identified in the first phase of the study, 43 were examined, and 15 were identified as high-priority SDHs for use in health-promotion interventions for informal settlement residents in the study area. They included individual health literacy, nutrition, occupational factors, housing-related factors, and access to public resources. Conclusions: Since identifying and addressing SDHs could improve health justice and mitigate the poor health status of settlement residents, ranking these determinants by priority using artificial intelligence will enable policymakers to improve the health of settlement residents through interventions targeting the most important SDHs.
The purpose of this study is to examine how the rural elderly assess the multi-dimensions of health. This includes the physical, mental and social dimensions of health, correlations among them, and whether there are gender differences and age-group (60's, 70's, 80 and over) differences. For this purpose, survey data was gathered from 881 rural elderly who live in a village. The statistical methods used for data analysis were descriptive statistics, t-test, ANOVA and correlations. The major findings of this study are as follows: Physical health was measured by self-rated health, ADL, IADL, and farmer symptoms. The respondents classified as 80 and over and female perceived their physical health more negatively. Mental health was perceived somewhat positively in general but female participants perceived their mental health more negatively than males. Social health of the rural elderly was relatively positive in comparison to the other dimensions of health. The rural elderly engaged in many social activities, and the majority of them had many social support networks composed of more than 12 persons. Relationships among the physical, mental and social health dimensions were different. The correlation of physical and mental health and the correlation of social and psychological health were statistically significant. However, the correlation of physical and social health had no significant meanings.
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