Relationship of Internet Addiction Severity with Depression, Anxiety, and Alexithymia, Temperament and Character in University Students
Bibliographic record
Abstract
The aim of the study was to investigate the relationship of Internet addiction (IA) severity with alexithymia, temperament, and character dimensions of personality in university students while controlling for the effect of depression and anxiety. A total of 319 university students from two conservative universities in Ankara volunteered for the study. Students were investigated using the Toronto Alexithymia Scale-20, the Temperament and Character Inventory, the Internet Addiction Scale, the Beck Anxiety Inventory, and the Beck Depression Inventory. Of the university students enrolled in the study, 12.2 percent (n=39) were categorized into the moderate/high IA group (IA 7.2 percent, high risk 5.0 percent), 25.7 percent (n=82) were categorized into the mild IA group, and 62.1 percent (n=198) were categorized into the group without IA. Results revealed that the rate of moderate/high IA group membership was higher in men (20.0 percent) than women (9.4 percent). Alexithymia, depression, anxiety, and novelty seeking (NS) scores were higher; whereas self-directedness (SD) and cooperativeness (C) scores were lower in the moderate/high IA group. The severity of IA was positively correlated with alexithymia, whereas it was negatively correlated with SD. The "difficulty in identifying feelings" and "difficulty in describing feelings" factors of alexithymia, the low C and high NS dimensions of personality were associated with the severity of IA. The direction of this relationship between alexithymia and IA, and the factors that may mediate this relationship are unclear. Nevertheless, university students exhibiting high alexithymia and NS scores, along with low character scores (SD and C) should be closely monitored for IA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".