Review on the Effects of Increased Social Media Use on Depressive Symptoms in Adolescents
Bibliographic record
Abstract
Social media (SM) allow individuals to connect with one another through online networking. SM has its benefits in regard to knowledge accumulation and effective communication with all persons at the global scale. However, increased use of SM can have detrimental effects among the adolescent population, specifically in terms of their mental health status. The purpose of the present review is to examine the literature in terms of the influence of increased SM use on the rates of depressive symptoms found in adolescents. During this review, approximately 40 articles were initially reviewed to examine whether or not they meet the primary evidence base criteria for the present literature review. The primary evidence base has been defined as follows: primary research articles published after 2015 in which DS in adolescents who use SM are examined. Based on these criteria, seven articles were located and reviewed. Overall, it has been generally found that an increase in SM use is associated with an increase in rates of depressive symptoms (DS) in adolescents. This finding is crucial as it brings forth the notion that SM may have a strong correlation with DS in a large percentage of adolescents globally. Hence, psychological experts (e.g. therapists, psychologists, clinical psychologists, psychiatrists) should consider investigating SM use levels from their clients prior to applying therapeutic interventions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".