Mood and suicidality amongst cyberbullied adolescents- a cross-sectional study from youth risk behavior survey
Bibliographic record
Abstract
Introduction There is a limited literature available showing mental health burden among adolescents following cyberbullying. Objectives Aim is to evaluate the association of low mood and suicidality amongst cyberbullied adolescents. Methods A study on CDC National Youth Risk Behavior Surveillance (YRBS) (1991-2017). Responses from adolescence related to cyberbullying and suicidality were evaluated. Chi-square and mix-effect multivariable logistic regression analysis was performed to find out the association of cyberbullying with sadness/hopelessness, suicide consideration, plan, and attempts. Results A total of 10,463 adolescents, 14.8% of adolescents faced cyberbullying a past year. There was a higher prevalence of cyberbullying in youths aged 15-17 years (25 vs 26 vs 23%), which included more females to males (68 vs 32%).(p<0.0001) Caucasians (53%) had the highest number of responses to being cyberbullied compared to Hispanics (24%), African Americans (11%).(p<0.0001) There was an increased prevalence of cyberbullied youths with 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 adolescence had a 155% higher chance of feeling sad and hopeless [aOR=2.55; 95%CI=2.39-2.72], considered suicide [1.52 (1.39-1.66)], and suicide plan [1.24 (1.13-1.36)]. Conclusions In our study, cyberbullying was associated with negative mental health outcomes. Further research is warranted to examine the impact and outcomes of cyberbullying amongst adolescents and guiding the policies to mitigate the consequences. Disclosure No significant relationships.
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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 | 0.001 |
| 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".