The Relationships between Compulsive Internet Use, Alexithymia, and Dissociation: Gender Differences among Italian Adolescents
Bibliographic record
Abstract
Internet Gaming Disorder, Internet Addiction, Problematic Internet Use and Compulsive Internet Use cause distress and significant impairment in important areas of a person's functioning, in particular among young people. The literature has indicated that males show higher levels of problematic internet use than females. People can use the internet to avoid or alleviate negative affects; in fact, problematic internet use is associated with alexithymia and dissociation. Few studies have focused on the different stages of adolescence, gender differences, and the relationships between the aforementioned variables. This research aims to fill this gap. Five hundred and ninety-four adolescents aged between 13 and 19 filled in the Compulsive Internet Use Scale, the Toronto Alexithymia Scale, the Adolescents Dissociative Experiences Scale, and other ad hoc measures. Surprisingly, females reported higher compulsive internet use compared with males. Moreover, they referred more difficulties/symptoms and greater levels of alexithymia than males. No differences across the stages of adolescence were found. Different strengths in the relationships between variables were found according to gender. Moderated mediation analyses indicated that dissociation is an important mediator in the relation between alexithymia and Compulsive Internet Use only among females. This study shed new light on gender differences around problematic internet use and some related risk factors, in order to identify and develop prevention and treatment programs to face this topical and relevant issue.
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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 | 0.001 | 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".