Examining the Link Between Problematic Smartphone Use and Substance Use Disorders Among College Students: Association Patterns Using Network Analysis
Bibliographic record
Abstract
This study examines the interplay between problematic smartphone use (PSU) and substance use disorders (SUDs) among Brazilian college students, also addressing associated emotional distress (e.g., depression, anxiety, and stress). A total of 3130 students (M_age = 23.6; SD_age = 5.34) participated in an online survey featuring validated measures for assessing PSU, alcohol and drug consumption, and emotional distress. Participants were categorized into problematic smartphone use (PSU) and non-problematic use (nPSU) groups. The prevalence of PSU was 46.9%, notably higher among female students, those lacking a religious affiliation, individuals living alone, and the unemployed. PSU individuals showed significantly elevated rates of alcohol, cannabis, and other illicit substance use, along with greater emotional distress. Network analysis revealed that weekly alcohol consumption and stress symptoms exhibited the highest centrality indices (e.g., stress with high betweenness; alcohol with strong expected influence), underscoring their key roles in connecting PSU and SUDs. These findings suggest that PSU and SUDs may share underlying emotional vulnerabilities, highlighting the need for integrated intervention strategies targeting both conditions concurrently.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".