Meta-Analysis: Effect of Bullying on the Risk of Suicide in Adolescents
Bibliographic record
Abstract
Background: Mental health disorders are among the most important public health problems globally. Bullying affects the lives of many children and young people. Studies in high-income countries have reported an association between bullying and suicidal behavior among school adolescents. For example, studies in various parts of the United States found that students who were bullied were significantly more likely to experience suicidal ideation, planning, and suicide attempts. This study aims to determine the effect of bullying on the risk of suicide in adolescents Subjects and Method: The meta-analysis was carried out according to the PRISMA flow chart and the PICO model. Q: Adolescent, I: Bullying, C: Not bullying, O: Risk of suicide. Search for articles in this study through databases that include PubMed, Google Scholar, Science Direct and Scopus. With keywords including: "bullying" AND "suicide" AND "adolescent". A full paper article with a cross-sectional study, the research subjects were adolescents, the relationship size used was the adjusted odds ratio, the research outcome was suicide risk. Analysis done with Revman 5.3 Results: A total of 11 articles with a cross sectional design from Israel, China, Vietnam, Tanzania, Canada, the United States, Iran and South Korea totaling 124,728 research samples. The results showed that adolescents who were bullied had a risk of suicide 2.70 times compared to those who did not experience bullying (aOR=2.70; 95% CI=1.88 to 3.87; p<0.001). Conclusion: Bullying can increase the risk of suicide in adolescents. Keywords: bullying, suicide, adolescent.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.063 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".