Gaming addiction among Tunisian adolescent
Bibliographic record
Abstract
Introduction Gaming is a source of addiction for adolescents. It is recognized as a behavioral and mental health condition, both by the American Psychiatric Association and by the World Health Organization. Objectives To determine the prevalence of gaming addiction among secondary school students. Methods This cross-sectional study was conducted between September and October of 2020 among students enrolled in secondary school. The participants had filled the Game addiction scale and a data file regarding the socio-demographic information, physical and information about the internet access and use. Results The initial sample was composed of 180 secondary school students. Among them 28 were excluded because they did not play video games. Final sample consisted of 152 students (90 males, 62 females) with a mean age of 13.14 ± 1.2 years. The average duration of connection among participants was 5.3 hours per day. Nearly one quarter of the participants (24,3%) played videogames more than 20 h per week. The prevalence of gaming addiction was 21,7%. The participants with gaming addiction were, on average, younger than those who were not addicted to gaming Game-addicted individuals were more likely to be male than female (13,8% vs 7,9%; p=0,036). There was, also, a significant relation between IA and having academic difficulties (p=0.042). Conclusions Based on our study findings, that gaming addiction is a challenging problem among Tunisian adolescents. We recommend authorities consider gaming addiction a serious problem for the young population and make this growing phenomenon an adolescent health priority. Disclosure No significant relationships.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".