Screen time woes: Social media posting, scrolling, externalizing behaviors, and anxiety in adolescents
Bibliographic record
Abstract
Adolescent screen time use increased significantly during the pandemic. Excessive social media and prolonged screen use are risk factors for internalizing and externalizing behaviors . However, limited understanding remains of pre-existing factors that predispose adolescents to adverse outcomes, and how quantitative (e.g., time spent) and qualitative (e.g., screen use behaviors) aspects relate to mental health , including anxiety and emotional/behavioral difficulties. Understanding these links is critical for evidence-based recommendations in healthcare and education. A community-based sample of 580 adolescents aged 12–17 years participated in an online survey from December 2022–August 2023. Demographic data, pre-existing vulnerabilities, screen use, emotional and behavioral difficulties and anxiety were collected using self-report questionnaires. The time spent on screens during weekdays and weekends, as well as screen-use behaviors such as frequency of use, total time, passive scrolling, and content posting on social media were analyzed. Notably, about 45 % of adolescents without pre-existing vulnerabilities reported anxiety in the clinical range. The odds ratio analysis showed that exceeding 2 h of screen time on weekdays doubled the odds of clinically-elevated anxiety and quadrupled the odds of experiencing emotional and behavioral difficulties. Although different aspects of screen use behaviors showed linear associations with mental health outcomes, passive scrolling had the strongest negative influence, even after controlling for age, gender, and pre-existing vulnerabilities, compared to active screen use or more general indicators (frequent and prolonged screen time). Managing screen time and activities based on individual mental health profiles, particularly regarding anxiety levels, may help support adolescent well-being.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".