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How Does Family Connectedness Contribute to Youths' Health? The Mediating Role of Coping Strategies.

Christine GervaisPaul E Jose
Published in: Family process (2019)
Family connectedness has important implications for adolescents' well-being, contributing to their physical, psychological, and social health. However, little is known about the mechanisms underlying these effects. The present longitudinal study examined the process by which family connectedness, as perceived by adolescents, predicted greater positive and fewer negative health behaviors in adolescents over time. In particular, we sought to determine whether adaptive and maladaptive coping strategies mediated the link between family connectedness and adolescents' self-reported health status. Data were obtained from 1,774 New Zealand adolescents aged 10-17 years, who completed a self-report survey three times at one-year intervals. With longitudinal mediation path models, we tested whether maladaptive and adaptive coping strategies at T2 functioned as mediators between family connectedness at T1 and overall health, vitality, sleep sufficiency, body satisfaction, substance use, and self-harm at T3. Findings revealed that family connectedness predicted greater levels of adaptive coping, which, in turn, predicted better health indicators but not decreases in ill-health indicators. Furthermore, family connectedness predicted lower maladaptive coping, which, in turn, predicted higher levels of positive health outcomes and fewer negative health outcomes. Results showed that the positive effect of family connectedness on adolescents' health occurred through increased use of adaptive coping strategies, decreased use of maladaptive coping strategies, or both. These results have important implications for practitioners working with adolescents and parents, as well as for health promotion program developers.
Keyphrases
  • physical activity
  • young adults
  • healthcare
  • health promotion
  • social support
  • public health
  • mental health
  • depressive symptoms
  • health information
  • primary care
  • cross sectional
  • big data
  • living cells
  • sensitive detection