The cost of firearm violent crime in British Columbia, Canada
Bibliographic record
Abstract
Introduction: This study aimed to quantify the total cost of violent firearm-related offenses in British Columbia in 2016 Canadian dollars over a five-year period, 2012 to 2016. The purposes of this study were to estimate the direct costs to the health care system and indirect costs to society for violent firearm injuries and deaths; and to estimate criminal justice system costs pertaining to firearm incidents. Methods: Human and economic costs to the health care system and productivity losses were calculated using health administrative datasets such as B.C. Vital Statistics and Discharge Abstract Database. Criminal justice system costs pertaining to firearm incidents were estimated by applying weighted average costs to aggregate expenditures using methodology consistent with that used by Statistics Canada. Results: There was a total of 108 deaths and 245 hospitalizations resulting from violent firearm injuries. The total estimated cost of all violent firearm crime averaged $294,378,985 per year; human costs averaged $188,416,841 per year, where health care costs averaged $3,910,317 per year, productivity losses from workforce and household averaged $17,299,054 and $4,559,470 per year, respectively, and loss of life averaged $162,648,000; and $105,021,145 in criminal justice system costs, and $941,000 in programming costs. Conclusion: This study clearly demonstrates the significant cost of violent firearm injury in British Columbia and the impacts on the health care system, criminal justice system, and to society at large, particularly within the criminal justice system where the costs were significantly higher than health care.
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 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".