Mood and Suicidality among Cyberbullied Adolescents: A Cross-Sectional Study from Youth Risk Behavior Survey
Bibliographic record
Abstract
Background: There is limited literature available showing the mental health burden among adolescents following cyberbullying. Objective: The aim was to evaluate the association between low mood and suicidality among cyberbullied adolescents. Method: A cross-sectional analysis of the data was performed among adolescents from the National Youth Risk Behavior Surveillance System. Responses from adolescents related to cyberbullying and suicidality were evaluated. Chi-square and mix-effect multivariable logistic regression analysis was performed to determine the association of cyberbullying with sadness/hopelessness and suicide consideration, plan, and attempts. Results: Of a total of 8274 adolescents, 14.8% of adolescents faced cyberbullying past year. There was a higher prevalence of cyberbullying in youths aged 15, 16, and 17 years (25%, 26%, 23%, respectively), which included more females than males (68% vs. 32%; p < 0.0001). Caucasians (53%) had the highest number of responses to being cyberbullied compared to Hispanics (24%) or African Americans (11%; p < 0.0001). There was an increased prevalence of cyberbullied youths, feelings of sadness/hopelessness (59.6% vs. 25.8%), higher numbers considering suicide (40.4% vs. 13.2%), suicide plan (33.2% vs. 10.8%), and multiple suicidal attempts in comparison to non-cyberbullied (p < 0.0001). On regression analysis, cyberbullied adolescents had a 155% higher chance of feeling sad and hopeless [aOR = 2.55; 95%CI = 2.39–2.72] and considered suicide [aOR = 1.52 (1.39–1.66)] and suicide plan [aOR = 1.24 (1.13–1.36)]. Conclusion: In our Study, cyberbullying was associated with negative mental health outcomes. Further research is warranted to examine the impact of cyberbullying among adolescents and guiding the policies to mitigate the consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".