The association between social network factors and mental health at different life stages
Survey
HILDA
Author(s)
Date Issued
2015-11-27
Pages
1-9
Keywords
Mental health
Abstract
Objectives: Psychosocial factors are important determinants of an individual’s health. This study examines the association between health scores and social network factors on mental health across different life stages.
Methods: Data was drawn from the Household Income and Labour Dynamics in Australia (HILDA) survey for adolescents (n = 1,739), adults (n = 10,309) and seniors (n = 2,287). Hierarchical regression modelling was applied to examine effects within and across age groups. All the variables were derived from the self-completion questionnaire.
Results: The social network factors were statistically significant predictors of mental health outcomes for all three life stages. For adolescents the three social network factors were statistically significant with social isolation having the largest impact (β = -.284, p < .001), followed by social connection (β =.084, p < .001) and social trust having a similar effect (β = .073, p < .001). For adults social isolation had the highest impact (β = -.203, p < .001), followed by social connections (β = .110, p < .001) and social trust (β = .087, p < .001).The results for seniors were social isolation (β = -.188, p < .001), social connection (β = .147, p < .001) and social trust (β = .032, p < .05).
Conclusions: After adding the social network factors the models improved significantly with social isolation playing the most significant role across all life stages whereas the other social network factors played a differentiated role depending upon the life stage. These findings have practical implications in the design of mental health interventions across different life stages.
Methods: Data was drawn from the Household Income and Labour Dynamics in Australia (HILDA) survey for adolescents (n = 1,739), adults (n = 10,309) and seniors (n = 2,287). Hierarchical regression modelling was applied to examine effects within and across age groups. All the variables were derived from the self-completion questionnaire.
Results: The social network factors were statistically significant predictors of mental health outcomes for all three life stages. For adolescents the three social network factors were statistically significant with social isolation having the largest impact (β = -.284, p < .001), followed by social connection (β =.084, p < .001) and social trust having a similar effect (β = .073, p < .001). For adults social isolation had the highest impact (β = -.203, p < .001), followed by social connections (β = .110, p < .001) and social trust (β = .087, p < .001).The results for seniors were social isolation (β = -.188, p < .001), social connection (β = .147, p < .001) and social trust (β = .032, p < .05).
Conclusions: After adding the social network factors the models improved significantly with social isolation playing the most significant role across all life stages whereas the other social network factors played a differentiated role depending upon the life stage. These findings have practical implications in the design of mental health interventions across different life stages.
Subjects
Type
Journal Articles
