The epidemiology and deprivation profile of firearm-related injuries and deaths in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Firearm-related injuries (FRI) are an important public health issue in Canada. This study aims to determine the incidence of FRI in British Columbia (BC) and examine the distribution according to demographics, intent, urban-rural residence and neighbourhood deprivation. METHODS: De-identified data on deaths and hospitalizations (2010-2019) were retrieved from the BC Vital Statistics and the Discharge Abstract Database obtained from the BC Ministry of Health. We implemented the Canadian Index of Multiple Deprivation for the dissemination area-level marginalization. RESULTS: A total of 1868 fatal and nonfatal FRI were included in our study, of which 46.4% were due to self-harm. The annual injury rate was 3.93 per 100 000, with the highest rates among men aged 15 to 34 years. Rates were highest in rural and remote areas, in neighbourhoods with the least diverse ethno-cultural composition, and the greatest level of situational vulnerability and economic dependency. We did not observe significantly different rates across residential instability quintiles. The marginalization pattern for intentional self-harm was similar to the aggregated deprivation profile. While assaults were more common in neighbourhoods with higher levels of situational vulnerability and more diverse populations, unintentional injuries were more prevalent in neighbourhoods with higher levels of situational vulnerability. CONCLUSION: This study revealed that the burden of FRI was not evenly distributed across demographic determinants, neighbourhood deprivation or urban-rural areas of residence throughout BC. We also observed different deprivation profiles across the various intents of injury and death. Findings highlight the need for addressing FRI at its root causes, by implementing system-level interventions focussed on suicide prevention, poverty reduction, and promoting employment and education.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".