Date of Award:


Document Type:


Degree Name:

Doctor of Philosophy (PhD)



Committee Chair(s)

Melanie M. Domenech Rodríguez


Melanie M. Domenech Rodríguez


Scott C. Bates


Rick A. Cruz


Ginger Lockhart


Aryn M. Dotterer


Mexico is experiencing increased rates of substance use among children and adolescents. This is concerning as early substance use is associated with an increased risk for developing mental and physical health problems during adulthood. These outcomes may be prevented through early identification and intervention before individuals encounter the negative consequences of substance use/abuse. The current dissertation sought to improve our knowledge regarding factors associated with substance use and intention for first time use among Mexican children. Three manuscripts examined child individual characteristics and aspects of their environment. The first manuscript examined demographic characteristics to determine whether particular groups of children were at increased risk for substance use and intensions for first time use. We found that being a boy, of indigenous background, non-religious, and over developmental age for grade were all associated with risk. The second manuscript focuses on examining parent characteristics and practices on substance use and intention for first time use. We found that parental illicit substance use was associated with the largest increases in risk and positive parenting was a protective factor. The third manuscript utilized machine learning, an algorithmic approach that predicts membership in one of two groups, to assist in the identification of high value factors that distinguish between substance users and non-users. Findings from this research identified factors associated with childhood substance use at individual and environmental levels. Being a boy and having a best friend or father that used illicit substances were the key indicators that could provide valuable information as screening questions. These findings provide valuable information needed to inform the development of early substance use prevention programs in Mexico. Results also suggest that machine learning may be an important tool in uncovering information that could bolster prevention efforts by improving our ability to identify children at risk for substance use. This research was supported by the Utah State University Psychology Department and School of Graduate Studies.



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Psychology Commons