Fatal and non-fatal firearm-related injuries in Canada, 2016–2020: a population-based study using three administrative databases
Bibliographic record
Abstract
Abstract Background Firearms are a substantial cause of injury-related morbidity and mortality in Canada and globally, though evidence from contexts other than the USA is relatively limited. We examined deaths, hospitalizations and emergency department (ED) visits due to firearm-related injuries in Canada to identify population groups at increased risk of fatal and non-fatal outcomes. Methods We conducted a population-based study using three national administrative databases on deaths, hospitalizations, and ED visits. ICD-10 codes were used to identify firearm-related injuries from January 1, 2016, through December 31, 2020. Fatal and non-fatal firearm injuries were classified as suicide/self-harm, homicide/assault, unintentional, undetermined or legal intervention injuries. We analyzed the data with counts, rates and proportions, stratified by sex, age group, province/territory, and year. Results Over the 5-year period, we identified 4005 deaths, 3169 hospitalizations, and 2847 ED visits related to firearm injuries in various jurisdictions in Canada. Males comprised the majority of fatal and non-fatal injury cases. The highest rates of fatal and non-fatal firearm injuries were among 20- to 34-year-olds. The leading cause of fatal firearm injuries was self-harm (72.3%). For non-fatal firearm hospitalizations and ED visits, assault (48.8%) and unintentional injuries (62.8%) were the leading causes of injury. Rates varied by province and territory. Conclusions Our results showed that males comprised the majority of fatal and non-fatal firearm injuries in Canada. The rates of both fatal and non-fatal firearm injuries were highest among the 20- to 34-year-old age group. This comprehensive overview of the epidemiology of firearm injuries in Canada provides baseline data for ongoing surveillance and policy evaluation related to public health interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.115 | 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 teacher head, 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".