Reliability of suicide statistics: key considerationsfor suicide research in Ontario, Canada
Bibliographic record
Abstract
Suicides tend to be under-reported (by 10 to 38% in Canada) since many suicides are ‘hidden’ and considered ‘undetermined death’ or ‘accidental death’ through poisoning or drowning. Three key issues identified during designing suicide studies on the general population in Ontario are: 1. Limitations of coronial method in differentiating suicides from deaths from drug self-intoxication; 2. Selection of ICD codes in suicide research excludes undetermined deaths; and 3. MAID (Medical assistance in dying) deaths are not counted as suicides. As the criteria for MAID are further expanded, even more suicides may be hidden by being counted as a ‘natural death’ albeit by MAID. The reliability of suicide statistics depends on the way a jurisdiction determines what is counted as a suicide. The Ontario approach of coronial determination and the standard ICD-code inclusions for suicide, mean that suicide counts often exclude suicides from injuries or poisoning. Suicide mortality statistics are likely being impacted as a result of legal and administrative changes from MAID, particularly as the criteria for MAID have, and will likely be further, expanded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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; both teacher heads agree on what is shown here.
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".