An Observational Study of Bullying as a Contributing Factor in Youth Suicide in Toronto
Bibliographic record
Abstract
OBJECTIVE: Bullying has been identified as a potential contributing factor in youth suicide. This issue has been highlighted in recent widely publicized media reports, worldwide, in which deceased youth were bullied. We report on an observational study conducted to determine the frequency of bullying as a contributing factor to youth suicide. METHOD: Coroner records were reviewed for all suicide deaths in youth aged between 10 and 19 in the city of Toronto from 1998 to 2011. Data abstracted were recent stressors (including bullying), clinical variables, such as the presence of mental illness, demographics, and methods of suicide. RESULTS: Ninety-four youth suicides were included in the study. The mean age was 16.8 years, and 70.2% were male. Bullying was present in 6 deaths (6.4%), and there were no deaths where online or cyberbullying was detected. Bullying was the only identified contributing factor in fewer than 5 deaths. The most common stressors identified were conflict with parents (21.3%), romantic partner problems (17.0%), academic problems (10.6%), and criminal and (or) legal problems (10.6%). Any stressor or mental and (or) physical illness was detected in 78.7% of cases. Depression was detected in 40.4% of cases. CONCLUSIONS: Our study highlights the need to view suicide in youth as arising from a complex interplay of various biological, psychological, and social factors of which bullying is only one. It challenges simple cause-and-effect models that may suggest that suicide arises from anyone factor, such as bullying.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".