Relationship between Bullying and Suicidal Behaviour in Youth presenting to the Emergency Department.
Bibliographic record
Abstract
OBJECTIVE: Increasing numbers of adolescents are visiting emergency departments with suicidal ideation. This study examines the relationship between bullying and suicidal ideation in emergency department settings. METHOD: A chart review was conducted for all patients under 18 years of age presenting with a mental health complaint to the emergency departments at Kingston General or Hotel Dieu Hospitals in Kingston, Canada, between January 2011 and January 2015. Factors such as age, gender, history of abuse, history of bullying, type and time of bullying, and diagnoses were documented. RESULTS: 77% of the adolescents had experienced bullying, while 68.9% had suicide ideation at presentation. While controlling for age, gender, grade, psychiatric diagnosis, and abuse, a history of bullying was the most significant predictor of suicidal ideation. Individuals in this study who reported cyber bullying were 11.5 times more likely to have suicidal ideation documented on presentation, while individuals reporting verbal bullying were 8.4 times more likely. CONCLUSIONS: The prevalence of bullying in adolescent patients presenting to emergency departments is high. The relationship found between suicidal ideation and bullying demonstrates that clinicians should ask questions about bullying as a risk factor for suicide ideation during the assessment of children and adolescents.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".