Prevalence and Characteristics of Ambulance Collisions, a Systematic Literature Review
Bibliographic record
Abstract
The risk of dying or being injured as a result of traffic collisions is higher for medical emergency responders than for other professional drivers. This systematic review synthesizes the literature regarding the collisions of ambulances, focusing on the prevalence and characteristics surrounding such events. Keywords including paramedics and traffic collisions were searched in papers available in PubMed from January 1990 to July 2021. Two independent reviewers screened the abstracts of 2494 papers and ended up with 93 full-text articles to assess for eligibility, of which 26 papers were finally kept for this review. There was a total of 18 studies conducted in the United States, followed by 3 in Turkey, 2 in Taiwan, 1 in both the United States and Canada, 1 in France, and 1 in Poland. There is a high record of injury and fatal collisions for ambulances compared to other commercial or similarly sized vehicles. Drivers less than 35 years old with low experience and a history of citations are more likely to be involved in such collisions. Ambulance collisions are more likely to happen in urban areas and intersections are the riskiest locations. Most collisions occur when the ambulance is responding to an emergency call (i.e., going to the patient or the hospital) and using lights and sirens. Tailored preventive policies and programs for improving paramedics’ safety should be sought to reduce the burden of these occupational collisions.
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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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".