A systematic review of social media impact on psychological well-being among children, adolescents, and young adults in arab countries
Bibliographic record
Abstract
BACKGROUND: Social media is a major part of daily life for youth worldwide, including in Arab countries. Although it offers avenues for connection and information, growing concerns exist around its psychological impacts. This review aims to synthesize existing evidence on the association between social media use and mental health outcomes among children, adolescents, and young adults in Arab countries. METHODS: We systematically searched PubMed, Scopus, EMBASE, and Web of Science for peer-reviewed studies published until October 2025. Inclusion criteria targeted observational studies involving participants aged ≤ 25 years from Arab League countries, focusing on the relationship between social media use and mental health indicators. Data were extracted and quality assessed using the Newcastle-Ottawa Scale adaptation for cross-sectional designs. RESULTS: Twenty-one cross-sectional studies, encompassing over 15,000 participants with a mean age ranging from 13 to 25 years and representing multiple Arab countries, met the inclusion criteria. Findings revealed consistent associations between high or problematic social media use and adverse mental health outcomes, including anxiety, depression, sleep disturbances, and academic decline. Several studies highlighted emotional investment and nighttime use as key mediators. Sleep disruption and academic interference were common outcomes across settings. CONCLUSION: High levels of emotionally charged social media use among Arab children, adolescents, and young adults are associated with significant psychological and functional challenges, emphasizing the need for culturally tailored digital literacy and early screening. However, the predominance of cross-sectional designs across the included studies limits causal inference and underscores the necessity for future longitudinal and experimental research.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".