Work-related collisions involving paramedics in Quebec, Canada: an analysis of contributing factors
Bibliographic record
Abstract
Objectives. This research aimed to describe the distribution and occurrence of work-related collisions involving paramedics across Quebec and compare these results with collisions of general vehicles. Methods. This retrospective study spanned 11 years of data (2010–2020) extracted from Société de l’assurance automobile du Québec (SAAQ) road safety statistics. Statistical tests including a paired t test and the Mann–Kendall test were used for temporal analysis of aggregated numbers of injury and non-injury collisions in 17 regions of Quebec. A descriptive analysis and logit regression were used to compare the various factors, e.g., crash and environmental characteristics associated with ambulance and general vehicle collisions. Results. A higher percentage of ambulance collisions occurred at intersections (43.32%), in 50 km/h speed limit zones (48.29%), in commercial areas (48.29%) and on all types of two-way roads (62.05%). Logit models indicate that there is a significant association (p < 0.05) between collision severity and a variety of factors, including asphalt conditions, collision types and locations. Conclusion. The study results are consistent with prior research showing that Quebec paramedics have comparable incidents and collision causes related to environmental, weather and road factors. Our findings suggest several specific areas for policymakers to focus on regarding ambulance collision reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".