The Effect of Bullying on Depression in Adolescents: A Meta-Analysis
Bibliographic record
Abstract
Background: Bullying can cause feelings of insecurity in adolescents with reduced social support and the need for acceptance in the environment and peers is not fulfilled. 16.8% of teenagers who are involved in cyberbullying have a risk of depression. Depressive disorders are common in adolescents, with a prevalence approaching 5.6% in adulthood. This study aims to examine the effect of bullying on depression in adolescents. Subjects and Method: This research is a meta-analytical study and a systematic review. The articles used were obtained from several electronic databases including PubMed, Science Direct and ProQuest. The articles used in this study are articles that have been published from 2010-2021. The research data search process used the search words “bullying and depression”, “bullying and depression and adolescent and cross-sectional study”, “bullying and depression and adolescent and cross-sectional study and adjusted odd ratio”. Results: The results of the meta-analysis study in this study contained 9 articles consisting of Saudi Arabia, Malaysia, Seychelles, United States, Massachusetts, Bosnia and Canada. Bullying can increase the risk of depressive disorder in adolescents (aOR= 2.43; 95% CI= 1.87 to 3.15; p<0.001). Conclusion: Bullying affects the risk of depression in adolescents. Keywords: teens, bullying, depression Correspondence: Fitriah. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: fitriahbaharuddin@gmail.com. Mobile: +6282350701936. Journal of Health Promotion and Behavior (2021), 06(02): 112-120 https://doi.org/10.26911/thejhpb.2021.06.02.04
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".