Social Media and Depressive Symptoms
Bibliographic record
Abstract
Abstract Depression among children and adolescents is linked to a variety of negative outcomes, but many causes of depression are either difficult or impossible to alter. It is vital to identify the controllable causes of depressive symptoms, including time spent on screen media. Many studies demonstrate associations between time spent on social media and depressive symptoms. Time-lag evidence also demonstrates an association, with rates of adolescent depression doubling during the period when smartphone and social media use became common. Experimental evidence indicates that cutting back on social media use diminishes depressive symptoms. Social media time may cause depressive symptoms via several mechanisms including displacing time spent on healthy activities, increased body image concerns, cyberbullying, reinforcing spirals of negative content, and interference with sleep. Future research should include experimental trials among adolescents, determine which types of screen activities and social media platforms are most strongly linked to depressive symptoms, explore which populations are most impacted, and determine effective interventions. Recommendations include educating parents and adolescents to keep devices out of their bedrooms overnight, not allowing children and younger teens to use social media, and encouraging parents to model appropriate limits around screen devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".