Relationship Between Alexithymia and Mobile Phone Addiction with an Emphasis on the Mediating Role of Anxiety, Stress, and Depression: A Structural Model Analysis
Bibliographic record
Abstract
Background: Since the beginning of mobile phone addiction, alexithymia, depression, anxiety, and stress have been mentioned as complications of Internet addiction in various studies; however, the relationship between these variables has not been well investigated. Objectives: This study was conducted to investigate the relationship between alexithymia and mobile phone addiction, emphasizing the mediating role of anxiety, stress, and depression. Methods: In this descriptive-analytical study, 412 students of Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, were included using an available sampling method in 2019. Data collection tools were the demographic questionnaire, Toronto Alexithymia Scale (TAS-20), Depression, Anxiety, Stress Scale (DASS-21), and Mobile Phone Addiction Index (MPAI). Statistical analysis was carried out using SPSS software (version 22) and Amos software (version 16). A significance level of less than 0.05 was considered. Results: Alexithymia was a predictive factor for mobile phone addiction. Additionally, it had a direct and significant effect on depression (β = 0.540, P < 0.001), anxiety (β = 0.500, P < 0.001), and stress (β = 0.53, P < 0.001). Depression (β = 0.452, P < 0.001), anxiety (β = 0.408, P < 0.001), and stress (β = 0.460, P < 0.001) had a positive and significant effect on cell phone addiction. Conclusions: In this study, alexithymia was a predictive factor for mobile phone addiction. Moreover, the variables of depression, anxiety, and stress play the role of a relative mediating variable between alexithymia and mobile addiction.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".