Bridging the Digital Divide: A Systematic Review of the Impact of Social Media on Adolescent Mental Well-being
Bibliographic record
Abstract
The pervasive use of social media among adolescents has sparked significant concern regarding its impact on mental well-being. However, the evidence is fragmented, often highlighting only risks or benefits in isolation. This systematic review synthesizes contemporary evidence to provide a holistic understanding of the multifaceted impact of social media on adolescent mental well-being, identifying both detrimental and supportive mechanisms. Following the PRISMA guidelines, a systematic search was conducted for peer-reviewed studies published between 2015 and 2024. The synthesis revealed a dual-edged impact. Key negative pathways included social comparison (leading to envy and low self-esteem), cyberbullying victimization, and sleep displacement while positive pathways included social support and belonging, especially for marginalized youth, and access to mental health literacy communities. The findings indicate that outcomes are not determined by usage alone but are critically mediated by user activity (active vs. passive use), individual vulnerabilities, and the quality of online interactions. We conclude that the impact of social media on adolescent mental well-being is complex and non-uniform. Moving beyond a simplistic “social media is harmful” narrative, this review highlights the need for targeted interventions, digital literacy education, and platform design reforms.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".