Suicide in Canada
Bibliographic record
Abstract
Objective: The aim of this study is to compare Canadian suicide rates with other external causes of death to examine potential poisoning misclassifications as a contributor to suicide underreporting. Method: The study used Statistics Canada mortality data from 2000 to 2011 to calculate sex-and age-specific ratios by external cause of injury codes. Results: The overall Canadian suicide rate, as well as the poisoning suicide rate, declined over the study timeframe by an average annual percentage change (AAPC) of 1.0% each year. However, unintentional and undetermined poisonings increased significantly during the timeframe. Unintentional poisoning mortality (primarily narcotics and hallucinogens, including opioids) increased in proportion to suicides for both sexes, although females were consistently higher. The undetermined death to suicide ratio was higher and increasing for females. Poisonings of undetermined intent increased over time to comprise 47% to 80% of the undetermined death category for males and females combined. Conclusions: Canadian poisoning suicide rates declined, in contrast to rising unintentional and undetermined poisoning mortality rates. This trend is similar to that of the United States, supporting the hypothesis that misclassification of poisoning deaths may also be an issue in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".