Cyberbullying victimization and suicidal ideation among in-school adolescents in three countries: implications for prevention and intervention
Bibliographic record
Abstract
Abstract Background Countries in South and Central America and the Caribbean are among the countries with the highest adolescent cyberbullying crimes. However, empirical evidence about the effect of cyberbullying victimization on suicidal ideation among in-school adolescents in these countries remains limited. The present study examined the association between cyberbullying victimization and suicidal ideation among in-school adolescents in Argentina, Panama, St Vincent, and the Grenadines. Methods A representative cross-sectional data from 51,405 in-school adolescents was used. Hierarchical logistic regression analysis was used to estimate the association between cyberbulling victimization and suicidal ideation. Results Overall, 20% and 21.1% of the adolescents reported cyberbullying victimization and suicidal ideation, respectively in the past year before the survey. Suicidal ideation was higher among adolescents who experienced cyberbullying victimization (38.4%) than those who did not experience cyberbullying victimization (16.6%). Significantly higher odds of suicidal ideation were found among adolescents who had experienced cyberbullying victimization than those who had not experienced cyberbullying victimization [aOR = 1.88, 95% CI: 1.77–1.98]. Conclusion This finding calls for developing and implementing evidence-based programs and practices by school authorities and other relevant stakeholders to reduce cyberbullying victimization among adolescents in this digital age. Protective factors such as parental support and peer support should be encouraged.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".