EMS Provider and Patient Safety during Response and Transport: Proceedings of an Ambulance Safety Conference
Bibliographic record
Abstract
Abstract The out-of-hospital setting is unique to health care and presents many challenges to providing safe, high-quality medical care in emergency situations. The challenges of the prehospital environment require thoughtful design of systems and processes of care. The unique challenges of ambulance safety may be met by analyzing systems and incorporating process improvements. The purposes of this paper are to 1) outline the nature of this problem, 2) introduce a framework for this discussion, 3) provide expert opinion from a two-day ambulance safety conference, and 4) propose a plan of action to address the safety issues identified in the literature and expert opinion at the conference. Utilizing the Haddon Matrix as a framework, we present the safety issues and proposed solutions for factors contributing to an injury event in the emergency medical services (EMS) transport environment: host, agent, physical environment, and social environment. Host refers to the person or persons at risk, in this case, the EMS personnel or the patient. The agent of injury refers to the energy exerted during the course of an injury, and may be modified to include unrestrained equipment that contributes to the injury. The physical environment refers to the characteristics of the setting in which the injury takes place, such as the roadway or the physical design of the ambulance. Finally, the social environment refers to the social, legal, and cultural norms and practices in the society, such as peer pressure and a culture that discourages the use of safety equipment.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".