A Kassani, M Gohari, M Mousavi, M Asadi Lari, M Rohani, M Shoja,
Volume 8, Issue 2 (9-2012)
Abstract
Background and Objectives: Social capital consists of
individuals' communicational networks, social norms such as mutual trust and
cooperation in social networks. The aim of this study was to develop a model to
assess the implication of different determinants such as age, gender,
occupational status, mental and physical health on social capital components to
draw a correlation network for social capital determinants.
Methods: For the purpose of this study, data
was used from ‘social capital' section of Urban HEART-1 survey, which included
22,500 households from all 22 districts of Tehran, who were approached in a
randomized multistage cluster sampling method. Path analysis is a statistical
method to test hypothetical causal models, which requires various causal (path)
diagrams. To demonstrate the causal models of social capital, the hypothetical
paths of various components were developed and the final model of social
capital was drawn using multiple regression analyses.
Results: Path analysis indicated that social
capital components are influenced by various variables: A) Individual trust, by
occupational status, marital status, and physical component of health-related
quality of life B) Cohesion and social support, by education, age, and marital
status C) Collective trust and associative relation, by family size, age and
physical health. Direct effect of these variables on social capital components
was more than their indirect effects (through mental health and physical
health).
Conclusion: Social capital components
are directly affected by occupational, marital, educational status, family
size, physical health and duration of local residency. Planning to improve
educational and occupational status, strengthening family bonds and provision
of local facilities, may improve social capital.
Z Naghibifar, H Soori, S Eskandari, A Razzaghi, S Khodajarim,
Volume 17, Issue 1 (5-2021)
Abstract
Background and Objectives: Quality of life is a valuable indicator for measuring people's health. The purpose of this study was to determine the predictors of quality of life in the staff of Shahid Beheshti University of Medical Sciences, Tehran, Iran using the path analysis model.
Methods: This cross-sectional study was performed on subjects participating in the Health Cohort Study of Shahid Beheshti University of Medical Sciences, Tehran, Iran in 2018. A demographic information form and standard quality of life, general health, physical activity and burnout scales were used for data collection. The SPSS version 24 and Amos version 24 were used for data analysis.
Results: A total of 770 individuals were selected for the study, of whom 345 (44.8%) were male. The mean age ± standard deviation of the participants was 42.6±8.4. Analysis of the quality of life pathway of the participants showed an appropriate model (RMSEA= 0.014, CFI=0.999, NFI = 0.991, TLI = 0.994, CMIN/DF = 1.146). In addition, general health (0.560) and physical activity (0.078) had a direct correlation and occupational burnout (-0.178) and age (-0.082) had an inverse correlation with quality of life.
Conclusion: The results of this study suggest that factors such as general health, physical activity, and burnout have an impact on the participants. Therefore, it is necessary to consider the factors affecting the quality of life.
