Suicides in Young People in Ontario Following the Release of “13 Reasons Why”
Bibliographic record
Abstract
Objective: “13 Reasons Why,” a Netflix series, included a controversial depiction of suicide that has raised fears about possible contagion. Studies of youth suicide in the United States found an increase on the order of 10% following release of the show, but this has not been replicated in other countries. This study aims to begin to address that gap by examining the relationship between the show’s release and youth suicide in Canada’s most populous province. Methods: Suicides in young people (under the age of 30) in the province of Ontario following the show’s release on March 31, 2017, were the outcome of interest. Time-series analyses were performed using data from January 2013 to March 2017 to predict expected deaths from April to December 2017 with a simple seasonal model (stationary R 2 = 0.732, Ljung-Box Q = 15.1, df = 16, P = 0.52, Bayesian information criterion = 3.09) providing the best fit/used for the primary analysis. Results: Modeling predicted 224 suicides; however, 264 were observed corresponding to 40 more deaths or an 18% increase. In the primary analysis, monthly suicides exceeded the 95% confidence limit for 3 of the 9 months (May, July, and October). Conclusion: The statistical strength of the findings here is limited by small numbers; however, the results are in line with what has been observed in the United States and what would be expected if contagion were occurring. Further research in other locations is needed to increase confidence that the associations found here are causal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".