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The Influence of Child Gender on the Prospective Relationships between Parenting and Child ADHD.

David H DemmerFrancis PuccioMark A StokesJane A McGillivrayMerrilyn Hooley
Published in: Journal of abnormal child psychology (2019)
The aims of the current study were to (i) explore the potential bidirectional, prospective relationships between parenting and child ADHD, and (ii) explore whether these relationships differed on the basis of child gender. Data were obtained from waves 1 (children aged 4- to 5-years) to 5 (children aged 12- to 13-years) of the Longitudinal Study of Australian Child (LSAC) dataset (child cohort). In order to examine dimensions of both mothers' and fathers' parenting, a subsample of nuclear families with mothers, fathers and children present at all waves was extracted (final sample = 1932; sons = 981, daughters = 951). Child ADHD measures included the hyperactive-impulsive subscale of the strengths and difficulties questionnaire for symptoms, and parent-report question for diagnosis. Mothers and fathers completed scales on dimensions of Angry, Warm and Consistent Parenting. A cross-lagged panel model demonstrated (i) higher child ADHD symptoms at wave 1 led to a global increase in less-than-optimal parenting at wave 2, and (ii) child ADHD symptoms and Angry Parenting shared a prospective, bi-directional relationship (whereby increases in one predicted increases in the other over time) during earlier years of development. Latent growth curve models demonstrated that increases in Angry Parenting across time were significantly predicted by increases in child ADHD symptoms. A logistic regression demonstrated that both mothers' and fathers' Angry Parenting at wave 1 significantly predicted an ADHD diagnosis in children at wave 3. No predictive relationships differed between child genders; thus, it appears these prospective pathways are similar for both sons and daughters.
Keyphrases
  • mental health
  • attention deficit hyperactivity disorder
  • autism spectrum disorder
  • working memory
  • young adults
  • machine learning
  • depressive symptoms
  • patient reported