251 Urban-rural disparities in the rate of firearm injuries in British Columbia, Canada
Bibliographic record
Abstract
Background Injuries and deaths attributable to firearms are an important public health problem in Canada. Previous research has demonstrated that this burden is unevenly distributed across socio-economic groups and urban-rural areas. In 2020, a considerable increase was noted in rates of firearm-related violent crime in some Canadian jurisdictions, including in southern rural British Columbia (B.C). Objective To determine the rates of firearm-related injuries (FRIs) across the urban-rural continuum in B.C. and to demonstrate the dominant intent and vulnerable groups across this spectrum. Methods De-identified data on the firearm-related deaths and hospitalizations among B.C. residents (2010–2019) were retrieved from B.C. Vital Statistics and Discharge Abstract Database (respectively), BC Ministry of Health, and obtained through the BC Injury Research and Prevention Unit (BCIRPU). Records pertaining to in-hospital deaths were removed to avoid double-counting of fatalities. Level of urbanization was determined according to the dissemination area of the place of residence and categorized according to 7-tier Community Health Service Area urban-rural designations (metropolitan, large-urban, medium-urban, small-urban, rural-hub, rural and remote). Rural and remote categories were further combined after the initial analysis, due to proximity of the corresponding rates, to facilitate comparisons. Results The annual rate of (combined fatal and non-fatal) FRIs was highest in remote-rural areas (8.00 per 100,000, 95% CI= 7.44–9.00), while large urban areas had the lowest rate (2.55, 95% CI= 2.19–2.97). The highest and lowest median age of injured individuals was observed in remote-rural and metropolitan areas, respectively (50 vs. 32 years). No significant differences were noted regarding the sex ratio of cases across the urban-rural spectrum (overall 11:1). While intentional self-harm comprised 67.3% of FRIs in remote-rural areas, the dominant intent in metropolitan areas was assault with 45.1%. This was consistent with the finding that 71.5% of injuries in remote-rural areas were fatal (vs. 41.2% in metropolitan) Conclusions The results demonstrated a significant disparity regarding the rate of FRIs across the urban-rural areas of B.C., driven by higher intentional self-harm among middle-aged men. These findings highlight the importance of specific preventive measures to decrease the burden of FRIs according to the urban-rural residence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".