Existing Science on Human Factors and Ergonomics in the Design of Ambulances and EMS Equipment
Bibliographic record
Abstract
Background: Emergency medical services (EMS) personnel face a disproportionally high risk for fatality and injury due to the nature of their work; and current ambulance and EMS equipment design standards do not adequately safeguard EMS personnel from sacrificing personal safety for patient care, a known human factors and ergonomic (HFE) design challenge. Despite the desire to include HFE interventions or considerations into a standard, the effectiveness of existing HFE interventions for EMS is unclear. Objective: Therefore, this study aimed to synthesize the peer-reviewed literature on the design features of patient compartments and EMS equipment that affect EMS personnel’s performance or well-being. Methods: A scoping review methodology was applied to systematically search and screen for relevant articles, and extract data. Three databases (EmBase, Scopus, and PubMed) were searched, and search results were screened for articles that pertained to the performance or well-being of EMS personnel when interacting with the patient compartment or its associated equipment. Results and Discussion: Of the 4,125 search results, 48 relevant articles were retained, and then sorted into one of 3 categories: general design, patient handling, and patient transport. It was concluded that, although research has progressed over the past 15 years, more research, development, and resources are needed. Newer generations of ambulances have not been shown to be safer during collisions and there is a knowledge gap in how occupants and contents of a patient compartment behave during a collision. Crash-tests have been performed with restrained occupants and supplies; however, that scenario is unrealistic in the field. While the existing literature provided initial ideas and innovations for improving the HFE of patient handling and patient transport, it is important for future research to convey findings in a manner that can be used to inform design standards.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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".