How firearm legislation impacts firearm mortality internationally: A scoping review
Bibliographic record
Abstract
Background: The literature on gun violence is broad and variable, describing multiple legislation types and outcomes in observational studies. Our objective was to document the extent and nature of evidence on the impact of firearm legislation on mortality from firearm violence. Methods: A scoping review was conducted under PRISMA-ScR guidance. A comprehensive peer-reviewed search strategy was executed in several electronic databases from inception to March 2024. Grey literature was searched for unpublished sources. Data were extracted on study design, country, population, type of legislation, and overall study conclusions on legislation impact on mortality from suicide, homicide, femicide, and domestic violence. Critical appraisal for a sample of articles with the same study design (ecological studies) was conducted for quality assessment. Findings: 5057 titles and abstracts and 651 full-text articles were reviewed. Following full-text review and grey literature search, 202 articles satisfied our eligibility criteria. Federal legislation was identified from all included countries, while state-specific laws were only reported in studies from the U.S. Numerous legislative approaches were identified including preventative, prohibitive, and more tailored strategies focused on identifying high risk individuals. Law types had various effects on rates of firearm homicide, suicide, and femicide. Lack of robust design, uneven implementation, and poor evaluation of legislation may contribute to these differences. Interpretation: We found that national, restrictive laws reduce population-level firearm mortality. These findings can inform policy makers, public health researchers, and governments when designing and implementing legislation to reduce injury and death from firearms. Funding: Funding is provided by the Strategy for Patient-Oriented Research (SPOR) Evidence Alliance and in part by St. Michael's Hospital, University of Toronto. Scoping review registration: Open Science Framework (OSF): https://osf.io/sf38n.
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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 | 0.028 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".