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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".