Internet Addiction among Students: cross-sectional study
Bibliographic record
Abstract
Introduction Conceptually, the internet has transformed the Earth into a vast information network village, significantly enhancing human experience through unprecedented availability and exchange of information. However, the potential adverse effects of internet addiction on human health have emerged as a major global concern. Objectives This study aimed to estimate the prevalence of internet addiction among students. Methods A cross-sectional, descriptive, and analytical study was conducted between October 2022 and January 2023 among students from various faculties in Sfax. Data were collected through a self-administered electronic questionnaire accessible online, created using the Google Forms application. The questionnaire explored sociodemographic and relational data. Internet addiction was assessed using the Internet Addiction Scale (IAS). Results The average age of the students was 25,62 ± 3,29 years, with a sex ratio of 1/5. Among the participants, 96% resided in urban areas, and 81,9% lived with their families. Nearly half of the students were from the Sfax Faculty of Medicine, and 64,4% were in the third cycle of their studies. The study found a mean total score of 74,27 +/- 21,25 on the IAS, indicating an estimated prevalence of internet addiction at 24,2%. Factors correlated with internet addiction included excessive internet use by family members (p=0,004) and poor adaptation to the faculty (p=0,03). Conclusions Internet addiction was prevalent in our student population. Exploring the characteristics associated with this addiction would undoubtedly assist in identifying the risks our students might face. Disclosure of Interest None Declared
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".