Involvement of alcohol and drugs in crashes with vulnerable road users in Canada
Bibliographic record
Abstract
In order to effectively address the needs of pedestrians, cyclists, and motorcyclists in Canada, an epidemiological profile of injuries for such vulnerable road users is required to inform prevention initiatives. The Traffic Injury Research Foundation (TIRF) has partnered with the Public Health Agency of Canada (PHAC) to conduct a comparative analysis of injuries relating to vulnerable road users. The primary goal is to present an up-to-date overview of crashes and injuries related to vulnerable road users in Canada. A second goal is to present an assessment of the role of alcohol and drugs in these crashes. TIRF maintains two databases from which information was drawn. First, the National Fatality Database is a comprehensive, pan-Canadian, multi-decade set of core data related to all fatal motor vehicle crashes. Second, TIRF also maintains the Serious Injury Database, which contains information on persons seriously injured in crashes and on all drivers involved in these crashes. These data were compared with those available to PHAC including PHACrs own Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), an injury surveillance system operating in the emergency departments of 11 paediatric and four general hospitals across Canada. Previous analyses have shown elevated instances of alcohol involvement among fatally injured pedestrians. Comparable results will be presented using the other data sources, both about alcohol as well as drug involvement. While the evidence may be limited, it can be concluded that the involvement of alcohol and drugs in crashes with vulnerable road users must not be underestimated. The available data will be discussed with a special emphasis on informing prevention and mitigation initiatives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".