Login / Signup

Parental influences on marijuana use in emerging adulthood.

Lucia E CardenasMaria L Schweer-CollinsElizabeth A Stormshak
Published in: Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43) (2021)
The purpose of this study was to gain a clearer understanding of the relation between parental relationship qualities and overall emerging adulthood (EA) marijuana use processes. The present study drew from an ethnically and socioeconomially diverse sample of EAs (ages 19-22) and their parents (n = 470) from the Pacific Northwest region. This study used parent-report and child-report data to capture measures of parenting and EA marijuana use outcomes. Latent Class Growth Analysis (LCGA) was used to model trajectories of marijuana use and risk factor analyses were used to examine how marijuana group membership varied by covariates and parental relationship qualities. Results revealed that lower levels of family cohesion and quality of parent-child communication were more likely to predict membership in the high-using groups and moderate-decreasing user groups in comparison to low-to-non users. Results also indicated that lower levels of frequency of parent-child communication were more likely to predict membership in the high-users group compared to the low-to-non users. Regarding parent knowledge of marijuana use, trends toward congruence and underestimation of EA marijuana use predicted membership in the high-using and moderate-decreasing groups compared to the low-to-non users. Study results indicate EAs in their early 20s may be more likely to engage in healthy decision-making regarding marijuana use in an environment that includes warm, supportive parent-child relationships where parents are aware of their EAs use without focusing on their EA's perceptions of risk. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
  • mental health
  • healthcare
  • primary care
  • decision making
  • emergency department
  • risk factors
  • type diabetes
  • adipose tissue
  • skeletal muscle
  • high intensity
  • artificial intelligence
  • quality improvement