Firearm-related injuries and deaths in Ontario, Canada, 2002–2016: a population-based study
Bibliographic record
Abstract
BACKGROUND: Firearm-related injury is an important and preventable cause of death and disability. We describe the burden, baseline characteristics and regional rates of firearm-related injury and death in Ontario. METHODS: We conducted a population-based cross-sectional study using linked data from health administrative data sets held at ICES. We identified residents of Ontario of all ages who were injured or died as a result of a firearm discharge between Apr. 1, 2002, and Dec. 31, 2016. We included injuries classified as assault, unintentional, self-harm or undetermined intent secondary to handguns, rifles, shotguns and larger firearms. The primary outcome was the incidence of nonfatal and fatal injuries resulting in an emergency department visit, hospital admission or death. We also describe regional and temporal rates. RESULTS: We identified 6483 firearm-related injuries (annualized injury rate 3.54 per 100 000 population), of which 2723 (42.3%) were fatal. Assault accounted for 40.2% (1494/3715) of nonfatal injuries and 25.5% (694/2723) of deaths. Young men, predominantly in urban neighbourhoods, within the lowest income quintile were overrepresented in this group. Injuries secondary to self-harm accounted for 68.0% (1366/2009) of injuries and occurred predominantly in older men living in rural Ontario across all income quintiles. The case fatality rate of injuries secondary to self-harm was 91.7%. Self-harm accounted for 1842 deaths (67.6%). INTERPRETATION: We found that young urban men were most likely to be injured in firearm-related assaults and that more than two-thirds of self-harm-related injuries occurred in older rural-dwelling men, most of whom died from their injuries. This highlights a need for suicide-prevention strategies in rural areas targeted at men aged 45 or older.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".