Simulation-Based Training and Its Use Amongst Practicing Paramedics and Emergency Medical Technicians: An Evidence-Based Systematic Review
Bibliographic record
Abstract
Objectives: This systematic review (SR) describes how simulation-based training (SBT) is utilized by paramedics and emergency medical technicians (EMTs). Data sources: PubMed, CINAHL, Cochrane CENTRAL, Scopus, Web of Science, and Google Scholar were searched from 2010 to 2021. Review methods: Standard SR methodology was utilized according to PRISMA guidelines. Eligibility criteria included English studies conducted in the United States or Canada published and published between 2010 and 2021. Study designs were somewhat heterogeneous and included quantitative, qualitative, and mixed-methods projects. The specific populations included paramedics and EMTs. Results: 595 articles were initially identified and reviewed, 25 of which met our inclusion criteria. Of them, the most common SBT areas of focus documented in the literature was general assessment and treatment (7 studies) and airway management (7 studies). The majority of the studies were conducted in a mobile simulation lab (6 studies), simulation centers (5 studies), and ambulances (5 studies). Many of the studies report simulations involving using manikins alone and a combination of manikins and simulated patients. Overall, 21 studies documented the use of high-fidelity simulation. 16 studies involved paramedics only, 8 involved both paramedics and EMTs, and one study involved only EMTs. Most of the impact of SBT appeared to be on objective measures such as performance, procedural success, and ability to identify errors, as well as subjective metrics such as perceived improvement in knowledge and skill. The degree of sustained impact of SBT on skill retention was not frequently reported, and direct enhancement in patient outcomes such as length-of-stay, or mortality were not documented in any of the studies. Conclusions: Paramedics and EMTs provide critically important, often lifesaving, prehospital care. However, the opportunities to enhance their skills are limited by several factors; most notably their undergraduate and certificate educational requirements, which are much ... (truncated)
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".