Cyberbullying victimization and suicidal ideation among in-school adolescents in three countries: implications for prevention and intervention
Bibliographic record
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".