Characterising firearm-related databases across Canada: opportunities for data linkage to inform understanding of injury burden and prevention
Bibliographic record
Abstract
Introduction: Firearm injuries are a significant public health issue in Canada, yet the broader consequences, particularly non-fatal injuries, remain under examined in research and policy discussions. These injuries impose long-term physical, psychological, and social burdens on survivors and create substantial economic costs. While firearm-related injury data are collected across health, justice, and policing sectors, the lack of integration between these datasets hampers a comprehensive understanding of the issue. Objectives: This study aims to explore opportunities for linking national, provincial, and municipal datasets on firearm-related injuries in Canada, focusing on data from healthcare, legal, and firearm-specific domains. Methods: A comprehensive search for publications related to firearms of Medline, Scopus, and Web of Science and grey literature up to February 2025 identified several relevant datasets, including health records, death registries, and crime databases. Results: We found that while valuable information exists, the datasets are siloed, limiting the ability to analyse firearm injuries holistically. Gaps in data, such as the psychological impact of firearm injuries and specific details on firearm ownership, further constrain research. Despite these challenges, linking healthcare, justice, and firearm data could offer critical insights into the epidemiology of firearm injuries, their long-term effects, and associated risk factors. Conclusions: Overcoming operational constraints related to privacy, data quality, and funding will be essential for advancing this research and informing evidence-based interventions to reduce firearm-related harm. Drawing from successful data integration initiatives in other jurisdictions, such as Sweden and Australia, this study advocates for the development of a cross-sectoral data linkage strategy to enhance firearm injury prevention and policy development in Canada.
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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 | 0.107 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.062 | 0.127 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".