Alcohol and Psychoactive Substance Use among University Students in Edirne and Related Parameters

A systematic review of research on illicit substance use and a meta-analysis of its prevalence among college students in Türkiye

In this study, we attempted to systematically review and meta-analysis the prevalence and risk factors for illicit substance use (ISU) in Türkiye. Based on the PRISMA 2020 statement and checklist, we considered only full-text research articles on ISU among college students (CS) in Türkiye. We searched relevant research via Web of Science, PubMed, MEDLINE, Scopus, ScienceDirect, Google Scholar, and ULAKBIM between 11.06.2022-12.31.2022. We applied double arcsin transformation to the prevalence values and used the LFK index and trim and fill method to measure publication bias. In addition, we generated doi and funnel plots of the prevalence values reported in the selected studies. Among systematically reviewed 53 studies, we identified and meta-analyzed ISU prevalence in 37 studies. The mean prevalence of ISU was determined to be 0.06 (95% CI: 0.04 to 0.07). We discovered that cannabis was the most commonly used substance, with an aggregate prevalence rate of 0.044 (95% CI: 0.026 to 0.066). Common risk factors for ISU were found to be gender, parental education, income, mental problems/suicide/attempted suicide, ISU in family members/relatives, smoking, and alcohol use. Despite the lower prevalence of ISU among students in Türkiye, the research findings emphasized that it has recently become a significant public health problem among the young.

College student Fear of Missing Out (FoMO) and maladaptive behavior: Traditional statistical modeling and predictive analysis using machine learning

This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO's relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.