Internet addiction in Gulf countries: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The prevalence of internet addiction (IA) varies widely in the Gulf Cooperation Council (GCC) countries (4%-82.6%). We aimed to assess the quality of IA studies from the GCC and pool their data to get an accurate estimate of the problem of IA in the region. METHODS: A systematic review of available studies was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. PubMed, Embase, and Cochrane Controlled Register of Trials were systematically searched; studies conducted in GCC countries (i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) with a validated instrument for internet addiction assessment were eligible. Ten studies were eligible for the systematic review, all of which were included in the meta-analysis. The Newcastle Ottawa Scale was used for quality assessment. RESULTS: Nine out of ten of the included studies had either adolescent and/or young adult participants (age < 25). Two studies were of 'good' quality, six were of 'satisfactory' quality, and two were of 'unsatisfactory' quality. The pooled internet addiction prevalence was 33%; it was significantly higher among females than males (male = 24%, female = 48%, P = 0.05) and has significantly increased over time (P < 0.05). DISCUSSION AND CONCLUSIONS: One in every three individuals in GCC countries was deemed to be addicted to the internet, according to Young's Internet Addiction Test. A root cause analysis focusing on family structure, environment, and religious practices is needed to identify modifiable risk factors.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".