Relationship between internet addiction and alexithymia among university students
Bibliographic record
Abstract
Purpose Epidemiological studies concerning internet addiction found that 50% of internet addicts also have other kinds of psychiatric disorders. This study aims to examine the relationship between alexithymia and internet addiction levels among Turgut Özal University students in Ankara, Turkey. Method University students (1,107 students; 452 students from 12 associate degree programs and 655 students from 10 undergraduate programs) participated in the study. The researchers used the personal information form, the Toronto Alexithymia Scale and the Internet Addiction Scale. The approval for the current study was received from the Turgut Özal University Medical Faculty Clinical Studies Ethics Committee. Results The number of the alexithymic students was 12.5% whereas the number of the students who were internet addicts was 13.5%. The internet addiction scores were higher among alexithymic individuals than the non-alexithymic (p<0.001). There was a significant difference in internet addiction average scores between male and female students (p=0.001). 'Difficulty identifying feeling' scores were higher among females whereas ''externally oriented thinking' scores were higher among males. Conclusion The study findings suggest that the internet addiction scores were significantly higher among alexithymic individuals than those who are non-alexithymic. The most obvious reason for this relationship may that alexithymic individuals try to regulate their emotional moods through addictive behavior. There is a need for more comprehensive studies on this subject in the literature.
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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) | 0.003 | 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".