Canadian Operational and Emotional Prehospital Readiness for a Tactical Violence Event
Bibliographic record
Abstract
Providing prehospital care poses unique risks. Paramedics are essentially the only medical personnel who are routinely at the scene of violent episodes, and they are more likely to be assaulted than are other prehospital personnel. In addition to individual acts of violence, emergency medical services (EMS) providers now need to cope with tactical violence, defined as the deployment of extreme violence in a non-random fashion to achieve tactical or strategic goals. This study reviewed two topics; the readiness of EMS crews for violence in their environment and the impact of violence on the EMS crew member. This latter also evaluated the access and effectiveness of emotional support available to caregivers exposed to violent episodes. The results of the survey indicate a significant lack of preparedness for situations involving tactical violence. A total of 89% of respondents either had never had such training or had been trained more than one year ago. Thirty-six percent of respondents had never engaged in a field exercise with other responding agencies, and 4.5% of respondents were not aware of who would be in charge in such an event. In addition, this study indicates that EMS crews are exposed to events with significant emotional impacts without access to appropriate training and adequate support.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.015 | 0.001 |
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".