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Parental Perceptions of Individual and Dyadic Adjustment as Predictors of Observed Coparenting Cohesion: A Cross-National Study.

Hervé TissotRegina Kuersten-HoganFrance FrascaroloNicolas FavezJames P McHale
Published in: Family process (2018)
Over the past 20 years, systemically guided approaches to understanding early family processes have helped to provide greater clarity concerning the interplay among individual, dyadic, and family level processes. Parental depression, marital functioning, and child adjustment in particular appear to be reliable predictors of coparental and family level functioning. Indeed, cohesion at the level of the family group covaries in theoretically meaningful ways with these indicators of individual and dyadic adjustment. In this study, two collaborating research groups (one in Switzerland, the second in the United States) partnered to examine whether similar patterns of relationships exist among individual and marital adjustment and coparenting processes in families of 4-year-old children. Using similar constructs but disparate and occasionally dissimilar measures, both groups measured parent-reported depression, marital satisfaction, and child behavior problems. Coparenting cooperation and warmth were observed during family interactions. Despite differences between samples and evaluation tools, similar results were found for the Swiss and U.S. samples. A model with depression, marital satisfaction, and child symptoms as predictors of a latent factor of observed coparenting cooperation and warmth showed good fit to data in both samples, suggesting the model was relevant for each. Parameter estimation showed that higher coparenting cooperation and warmth was predicted by lower maternal depression and higher child internalizing symptoms. The common significant effects despite differences in assessment paradigms and instrumentation are of substantive interest. Future directions pertinent to the coparenting questions addressed in this research are discussed.
Keyphrases
  • mental health
  • sleep quality
  • depressive symptoms
  • healthcare
  • primary care
  • electronic health record
  • body mass index
  • machine learning
  • weight loss
  • big data
  • gestational age