Suicide Mortality in Canada after the Death of Robin Williams, in the Context of High-Fidelity to Suicide Reporting Guidelines in the Canadian Media
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that suicide mortality increases after high-profile suicide deaths. Indeed, suicide in the United States increased disproportionately after the suicide by suffocation of well-known comedian Robin Williams in August 2014. Such increases are often attributed to irresponsible media coverage of the suicide contributing to "copycat suicides." However, recent research indicates that the mainstream Canadian media have significantly improved their suicide coverage, with high fidelity to suicide reporting guidelines after Williams' death. As such, the aim of the present study is to examine suicide mortality in Canada after Robin Williams' suicide. METHODS: We obtained deidentified monthly suicide count data from January 1999 to December 2015 stratified by age, sex, and method of suicide from Statistics Canada. We used time-series analyses to estimate the expected number of suicides in the months following Robin Williams' death. This was done using a seasonal autoregressive integrated moving averages (SARIMA) method. Expected suicides were then compared with observed suicides. RESULTS: August 2014 was the month with the highest number of suicides from 2010 to 2015. The time-series model indicated a 16% increase in the expected number of suicides during the months from August to December 2014 inclusive. Moreover, males over 30 had the greatest number of excess suicides, and suicides by suffocation (the method used by Robin Williams) were also higher in August and the following months. INTERPRETATION: Suicides increased in Canada after Robin Williams' death, despite the improved mainstream media coverage witnessed in other studies. Other factors (e.g., social and alternative media) may have contributed to the observed increase in suicide.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".