Exploring the Links Among Risky Substance Use, Problematic Internet Use, and Academic Outcomes in University Freshmen: The Role of Mediating Factors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participants
2.3. Instruments
- World Health Organization’s Alcohol, Smoking, and Substance Involvement Screening Test v3.0 (ASSIST): this is a self-report questionnaire (Barreto et al., 2014) designed to detect and manage substance use. It includes eight questions covering ten substances: tobacco, alcohol, cannabis, cocaine, amphetamine-type stimulants, inhalants, sedatives, hallucinogens, opioids, and “other drugs”. An example item is “In your life, which of the following substances have you ever used?”. Each substance receives a score from 0 to 36, categorized as low, moderate, or high-risk use (with higher scores corresponding to higher risk). In this study, tobacco was excluded from the analyzed substances, primarily due to its distinct social context and less acute potential for immediate academic impairment compared to alcohol and illicit substances. In our sample, 17.9% of participants exhibited a moderate-to-high-risk pattern of use for at least one substance. Specifically, 11.5% reported moderate-to-high-risk alcohol use (cut-off score = 10), 7.1% reported moderate-to-high-risk cannabis use, and 3.2% reported a moderate-to-high-risk sedative use (cut-off score = 3 for both cannabis and sedatives). For all other substances, the proportion of moderate-to-high-risk users was below 0.5%.
- Internet Abusive Use Questionnaire (IAUQ, Calvo-Francés, 2016): this is a 12-item scale rated on a five-point Likert scale from 0 (“Totally disagree”) to 4 (“Totally agree”), assessing problematic internet use. An example item is “You lose sleep in order to stay online”. The total score ranges from 0 to 48, with the established cutoff of 24 or higher indicating problematic internet use. In this sample, the overall Cronbach’s alpha value was 0.91, and 10.7% of students scored above this cutoff.
- An adapted form of the Academic Motivation Scale (AMS, Biasi et al., 2017): this was developed based on the Self-Determination Theory from Vallerand et al. (1992), it assesses motivation through five subscales. Each subscale is composed of four items rated on an 11-point Likert scale ranging from 0 (“Not at all true”) to 10 (“Completely true”). The five subscales are (i) Amotivation (i.e., lack of motivation, example item: “I can’t see why I go to school and, frankly, I couldn’t care less”), (ii) External Regulation (i.e., motivation driven by external rewards, punishments, or demands, example item: “Because someone else expects me to”), (iii) Introjected Regulation (i.e., motivation driven by internal pressure like guilt or shame, example item: “To prove to myself that I am capable of completing my high-school degree”), (iv) Identified Regulation (i.e., motivation based on personal values and conscious choice, example item: “Because eventually it will enable me to enter the job market in a field that I like”), and (v) Intrinsic Regulation (i.e., motivation arising from inherent interest, enjoyment, and satisfaction in the academic experience, example item: “Because my studies allow me to continue to learn about many things that interest me”). The total score ranges from 0 to 40, with a higher score indicating a greater adherence to the construct represented by each subscale. In our sample, the overall Cronbach’s alpha values for the five subscales ranged from 0.78 to 0.94.
- Perceived School Self-Efficacy Scale (SASP, Biasi et al., 2017; Pastorelli & Picconi, 2001): this measures students’ perceptions of their ability to regulate and focus on their studies. It includes nine items rated on a five-point Likert scale ranging from 1 (“Not capable at all”) to 5 (“Fully capable”). An example item is “Focus on your studies without getting distracted”. The total score ranges from 9 to 45, with a higher score indicating a greater perceived level of self-efficacy. In this sample, the overall Cronbach’s alpha value was 0.88.
- University Connectedness Scale (UCS, Stallman & Shochet, 2008): this measures students’ perceived support and sense of belonging within their university. It consists of 18 items rated on a seven-point Likert scale, ranging from 1 (“Not at all”) to 7 (“All the time”). An example item is “Class sizes are so large that I feel like a number”. The total score ranges from 18 to 126, and higher scores indicate a stronger sense of connectedness and support. In our sample, the overall Cronbach’s alpha value was confirmed at 0.88.
- Freshmen’s dropout intentions were assessed using a composite score adapted from Hardre and Reeve (2003) for the Italian university context (Biasi et al., 2017). Students were asked how often they (1) Think about dropping out of college and pursuing something else; (2) Feel insecure about continuing their college studies year after year; (3) Consider the idea of discontinuing their college education; and (4) Intend to drop out of college. Each item was rated on a five-point Likert scale ranging from 1 (“Never”) to 5 (“Always”), with higher scores indicating greater dropout intentions. The dropout intention score was calculated as the mean of these four items (De Vincenzo, 2024).
2.4. Outcome Definition
- Academic performance, measured as GPA (continuous variable).
- Dropout intentions, a continuous score ranging from 1 to 5, where higher values indicate greater dropout intentions.
2.5. Statistical Analysis
- Step 1: Assess the association between the independent variable (i.e., risky substance use and problematic internet use) and the dependent variable (i.e., GPA and dropout intentions). This initial assessment establishes the c’ path, representing the direct effect of the independent variable (X) on the dependent variable (Y) before including the mediator (M, i.e., academic engagement factors).
- Step 2: Assess the association between X and M. This step determines the strength and direction of the a path, verifying that X has an effect on the proposed M.
- Step 3: Assess the association between M and Y while simultaneously controlling for X. This step evaluates the b path, confirming whether M significantly influences Y beyond the effect of X.
3. Results
3.1. Correlation Analysis Among Primary Outcomes, Substance/Internet Risky Use, and Academic Engagement Variables
3.2. Mediation Models for Academic Performance
3.3. Mediation Models for Dropout Intentions
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
GPA | Grade Point Average |
ASSIST | World Health Organization’s Alcohol, Smoking, and Substance Involvement Screening Test v3.0 |
IAUQ | Internet Abusive Use Questionnaire |
AMS | Academic Motivation Scale |
SASP | Perceived School Self-Efficacy Scale |
UCS | University Connectedness Scale |
SD | Standard Deviation |
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Variable | N | % |
---|---|---|
Field of study | ||
Medicine | 302 | 41.9 |
Engineering | 157 | 21.8 |
Economics | 180 | 25 |
Law | 46 | 6.4 |
Pharmacy | 23 | 3.2 |
Agricultural | 13 | 1.8 |
Employment status | ||
Student | 473 | 65.6 |
Working student | 248 | 34.4 |
Mean | SD | |
Grade Point Average 1 | 24.80 | 2.80 |
Percentage of attended lessons | 78.67 | 27.85 |
Mean number of hours spent studying per day | 3.90 | 2.03 |
Questionnaire | Mean | SD | Scale Range |
---|---|---|---|
ASSIST alcohol score | 4.71 | 5.04 | 0–36 |
ASSIST cannabis score | 0.84 | 3.33 | 0–36 |
ASSIST sedative score | 0.43 | 2.65 | 0–36 |
IAUQ total score | 12.43 | 9.42 | 0–48 |
AMS—subscales’ scores | 0–40 | ||
Amotivation | 6.21 | 7.67 | |
External regulation | 5.99 | 5.69 | |
Introjected regulation | 20.87 | 10.49 | |
Identified regulation | 31.20 | 9.60 | |
Intrinsic regulation | 30.70 | 8.21 | |
SASP total score | 28.75 | 6.01 | 9–45 |
UCS total score | 82.93 | 17.07 | 18–126 |
Dropout Intentions | 2.18 | 1.01 | 1–5 |
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Dagani, J.; Buizza, C.; Ferrari, C.; Rainieri, G.; Ghilardi, A. Exploring the Links Among Risky Substance Use, Problematic Internet Use, and Academic Outcomes in University Freshmen: The Role of Mediating Factors. Eur. J. Investig. Health Psychol. Educ. 2025, 15, 105. https://doi.org/10.3390/ejihpe15060105
Dagani J, Buizza C, Ferrari C, Rainieri G, Ghilardi A. Exploring the Links Among Risky Substance Use, Problematic Internet Use, and Academic Outcomes in University Freshmen: The Role of Mediating Factors. European Journal of Investigation in Health, Psychology and Education. 2025; 15(6):105. https://doi.org/10.3390/ejihpe15060105
Chicago/Turabian StyleDagani, Jessica, Chiara Buizza, Clarissa Ferrari, Giuseppe Rainieri, and Alberto Ghilardi. 2025. "Exploring the Links Among Risky Substance Use, Problematic Internet Use, and Academic Outcomes in University Freshmen: The Role of Mediating Factors" European Journal of Investigation in Health, Psychology and Education 15, no. 6: 105. https://doi.org/10.3390/ejihpe15060105
APA StyleDagani, J., Buizza, C., Ferrari, C., Rainieri, G., & Ghilardi, A. (2025). Exploring the Links Among Risky Substance Use, Problematic Internet Use, and Academic Outcomes in University Freshmen: The Role of Mediating Factors. European Journal of Investigation in Health, Psychology and Education, 15(6), 105. https://doi.org/10.3390/ejihpe15060105