Alexithymia and its Association with Smartphone Addiction and Physical Activity in University Students of Islamabad
Bibliographic record
Abstract
Objective: Determine the prevalence of alexithymia and its relationship with smartphone addiction and physical activity among university students of Islamabad. Methods: In this cross sectional study, a 20 item Toronto Alexithymia Scale (TAS-20), Smartphone Addiction Scale-Long version (SAS-LV), and International Physical Activity Questionnaire-Short form (IPAQ-SF) were administered to 377 students from 6 universities of Islamabad using convenient sampling technique and the data was analyzed through statistical package for social sciences 26. Results: Alexithymia was present in 29.7% of university students and it was positively correlated with smartphone addiction but not significantly associated with physical activity. The two factors in the subscales of alexithymia DIF and DDF were positively correlated with smartphone addiction whereas EOT was not significantly correlated. Moreover, other sociodemographic variables showed a positive relationship with alexithymia: age, gender, satisfaction with life and residential status. Practical implications: This study would increase the awareness about alexithymia and its associated factors in the local community which would further steer direction to seek help from healthcare practitioners. Conclusion: More than 1/4rth of the university students were suffering from alexithymia and the score of alexithymia increased with level of smartphone addiction. Keywords: Alexithymia, University students, Islamabad, Smartphone Addiction Scale-Long version, International Physical activity questionnaire.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".