The effect of legislation on firearm-related deaths in Canada: a systematic review
Bibliographic record
Abstract
BACKGROUND: Firearm misuse is common in cases of homicide, suicide and unintentional injury; this is a major public health issue, with societal and economic costs extending beyond the immediate injury or loss of life. We sought to review the evidence on the effectiveness of Canadian legislation in reducing deaths caused by firearms. METHODS: Five databases (PubMed, Embase, CINAHL, Web of Science and Scopus) were searched from inception to May 2021 for studies evaluating the effect of Canadian gun control laws Bill C-51 (1977), Bill C-17 (1991) and Bill C-68 (1995) on rates of firearm-related death. Two reviewers performed article screening independently and in duplicate. We synthesized data using descriptive statistics. The primary outcome of interest was firearm-related mortality rates. Because of study heterogeneity, a meta-analysis was not performed. RESULTS: Overall, 1479 articles were screened, and 18 studies were included. Ten studies examined the effect on homicides, of which 5 reported a reduction during the postlegislation period; 1 study reported evidence of substitution from firearms to other methods of homicide among people aged 15-24 years. Eleven studies evaluated the effect on suicides, with 9 finding a reduction in suicide rates. Eight of these studies reported evidence of substitution from firearms to other suicide methods. Two studies investigated accidental deaths; neither reported any benefit after legislation. INTERPRETATION: Evidence supporting the effectiveness of Canadian firearms legislation in the reduction of homicide and accidental death rates is inconclusive; a decrease in firearm-related suicide rates was observed by most studies, but evidence of method substitution was also identified. Re-evaluation of existing laws may be beneficial to build an improved and effective evidence-based national framework for prevention of gun violence. PROSPERO REGISTRATION: CRD42020192486.
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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".