Relationship Between Alexithymia, Smartphone Addiction, and Psychological Distress Among University Students: A Multi-country Study
Bibliographic record
Abstract
Objectives: Increasing dependence on smartphones results in the appearance of psychological problems, especially among young people. This study aims to determine the rates of alexithymia and its relationship with smartphone addiction and psychological distress in university students. Methods: A total of 2616 students (mean age = 22.5±3.5 years; 73.1% female) from universities in Egypt, Oman, and Pakistan were included in a cross-sectional and comparative study conducted through a web survey during the COVID-19 pandemic from October to December 2021. The following scales were used: Toronto Alexithymia Scale (TAS-20), Depression Anxiety Stress Scale (DASS-21), and Smartphone Addiction Scale-Short Version (SAS-SV). The survey also included questions related to sociodemographic and smartphone usage patterns.Results: Students scoring above the TAS-20 cutoff point were significantly more likely to have smartphone addiction (χ2(1) = 45.41; p < 0.001) and psychological distress (χ2(1) = 246.31; p < 0.001). Likewise, smartphone addiction was significantly associated with psychological distress (χ2(1) = 57.46; p < 0.001). However, at each of the TAS-20, SAS-SV, and DASS-21 variables, there were significant differences between the students of the three countries (p < 0.050, p < 0.010, and p < 0.010, respectively); smartphone addiction was highest in Oman, while alexithymia and psychological distress were most severe in Egypt. Women scored higher than men on SAS and TAS scales (p < 0.001). Students who used social media frequently were more prone to smartphone addiction. Conclusions: Understanding cultural and socioeconomic factors (such as living standards, technology accessibility, and social interaction patterns) is crucial for generating strategies to improve the psychological well-being of the youth of different regions and countries. Further, this study confirms the findings of recent studies indicating the heightened university students’ psychological vulnerability during the COVID-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".