The Relationship Between Social Media Use, and Mental Health Disorders in Adolescents and Young Adults: A Scoping Review
Bibliographic record
Abstract
Background Adolescents and young adults (AYAs) are particularly vulnerable to mood and anxiety disorders. Social media use has been linked to mental health problems in this age group, and its use has increased since the COVID-19 pandemic. The primary aim of this scoping review was to examine the relationship between social media use and mental health symptoms among AYAs during the COVID-19 pandemic. Exploratory aims were to identify factors that may mediate the association between social media use and mental health disorders. Methods A comprehensive literature search comprised of social media, anxiety, depression, and adolescents and young adult domains was conducted in OVID Medline and Embase. The search was limited to articles published from 2020 to 2023. Results Sixteen studies met the inclusion criteria and were analyzed. Social media use was positively associated with severity of depression and anxiety. Furthermore, the presence of online discrimination, self-comparison, reliance on social media for social approval, and cyberbullying were associated with poorer mental health outcomes in AYAs. Conclusion Social media use, and related factors, exhibited negative impacts on the mental health of AYAs. This scoping review highlights the repercussions of the pandemic on both social media use and mental health outcomes among AYAs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".