Closing the knowledge gap: identifying research priorities 1for firearm-related injury and mortality in Canada
Bibliographic record
Abstract
INTRODUCTION: Firearm-related injury and death are leading yet preventable causes of premature death in Canada. Our objective was to identify knowledge gaps and research priorities to inform a national research agenda to prevent firearm-related injury and death. METHODS: In a two-stage process, nominal group technique was used to encourage experts in firearm injury and death (N = 15) to generate ideas relevant to knowledge gaps in three areas: unintentional firearm injury, intimate partner violence (IPV)/femicide and other firearm-related assaults. Relevant parties (N = 43) subsequently voted on the identified gaps to determine top priorities for future research. RESULTS: In Stage 1, the experts identified 22 knowledge gaps in unintentional firearm injury, 16 in IPV-related firearm injury/femicide and 33 in other assault-related firearm injuries. Based on their importance and feasibility as research projects, they then selected five, three and seven, respectively, of these knowledge gaps. In Stage 2, the top priorities for future research emerged: the economic cost of firearm injuries to victims' families and communities and Canadian society; the impact of social policies and legislation aimed at reducing IPV/femicide-related firearm injuries and deaths; and a description of the available and required Canadian firearm-injury data. CONCLUSION: The top priorities highlight the large and diverse gaps in knowledge about firearm injury and death in Canada. This marks the first step toward developing a national research agenda for firearm-related injuries. Next steps include operationalizing these gaps into research questions, identifying data sources and methodological approaches, and choosing knowledge translation strategies.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".