Date of Graduation

12-2024

Document Type

Thesis

Degree Name

Master of Arts in Psychology (MA)

Degree Level

Graduate

Department

Psychological Science

Advisor/Mentor

Shields, Grant

Committee Member

Leong, Josiah K.

Second Committee Member

Veilleux, Jennifer C.

Keywords

addiction; decision-making; emotion regulation; personality; stress

Abstract

Substance use disorders (SUDs) are some of the most prevalent issues facing society. As SUDs are highly individualized in how they manifest, recent work has attempted to classify distinct profiles of drug users to better understand what factors put someone at risk for shifting from casual substance use to developing a substance use disorder (SUD). Common risk factors for SUD development include stressor exposure, difficulties with emotion regulation, personality, and risky decision-making. As many of the behaviors that constitute SUDs are exhibited prior to potential diagnosis, developing “profiles” that combine these traits and experiences could eventually predict SUDs. The current study (n = 266) used supervised machine learning to examine psychological traits and stressful life experiences as predictors into random forests that aimed to classify individuals as users or nonusers of specific substances. Each substance’s use was accurately predicted at a rate above chance. Additionally, the same variables used in the random forests were then considered as predictors of the severity of use of alcohol, cannabis, and nicotine using a more traditional statistical approach (i.e., ANOVAs). High degrees of stress and mismatches between developmental and recent stress, certain personality traits, and emotion regulation strategies were associated factors with severity of substance use. These findings represent a potential avenue for developing individualized profiles of addiction that could help in identifying how substance use escalates in severity.

Download

Included in

Psychology Commons

Share

COinS