Bibliographic record
Abstract
Background: Ambulance drivers are more likely to be involved in fatal or injury collisions compared to other professional drivers. Study Objective: This study is a retrospective study aimed to describe factors involved in paramedics’ collisions. Method: Spanning over 10 years of data (2010-2019) from a paramedic agency covering Montreal (Qc, Canada), links between the number of ambulance injuries and non-injury collisions and diverse characteristics like experience, sex, and age of paramedics, day and time of the collision, weather and surface conditions, type of environment, and type of driving activity. The distribution of characteristics involved in the severity of collisions is presented with descriptive analysis. The evaluation of trends of monthly and yearly ambulance collisions is conducted using the Mann-Kendal test. The logit model is also used to examine the effect of such factors on the odds of collision severity. Results: The results show although there is no significant reduction trend for the monthly ambulance collisions, the trend of incidence of annual non-injury collisions per paramedic is significantly decreasing. Also, young drivers with less experience are more involved in multiple collisions compared to their experienced colleagues. Furthermore, 62% of injury collisions happened when paramedics are responding to an emergency call. The logit model confirms a decrease in the odds of injury collisions (odds ratio: 0.48) during non-emergency activities. Also, intersections and traffic lights are the riskiest locations regarding injury collisions (43.5%, and 51%, respectively). In this case, collisions occurring at traffic lights can increase the odds of severity by 597%. Conclusion: This study exemplifies that preventive policy regarding paramedics (e.g., training programs) should focus on younger and less experienced paramedics, and risky locations, especially while driving on emergency calls. More oriented awareness and training programs for emergency respondents are required to reduce the number of work-related collisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".