Association of screen-based activities and risk of self-harm and suicidal behaviors among young people: A systematic review and meta-analysis of longitudinal studies
Bibliographic record
Abstract
Emerging evidence suggests that screen-based activities are associated with self-harm and suicidal behaviors. This study aimed to examine these associations among young people through a meta-analysis. We systematically searched EBSCO pshyARTICLES, MEDLINE (via PubMed), EMBASE, and Web of Science from their inception to April 1, 2022, and updated on May 1, 2024. Longitudinal studies reporting the association between various screen-based activities and subsequent self-harm and suicidal behaviors in young people aged 10 to 24 were included. Nineteen longitudinal studies were included in the qualitative synthesis, and 13 studies comprising 43,489 young people were included in the meta-analysis, revealing that total screen use is significantly associated with the risks of self-harm and suicidal behaviors. Cyberbullying victimization was also related to these adverse outcomes. Subgroup analyses indicated that social media use and problematic screen use are significant risk factors for self-harm and suicidal behaviors. Study quality was appraised using the Newcastle-Ottawa Scale, and potential publication bias was deemed unlikely to affect the results significantly. These findings suggest that screen-based activities should be considered in the management and intervention strategies for self-harm and suicidal behaviors in young people.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".