Evaluating the Effectiveness of Trauma Care and Emergency Preparedness Training Programs on Prehospital Primary Survey Skills: A Systematic Review
Bibliographic record
Abstract
This systematic review evaluates the impact of trauma care and emergency preparedness training programs on prehospital primary survey effectiveness. A comprehensive search strategy was employed across multiple databases, including PubMed, Cochrane Library, Embase, and the Cumulated Index to Nursing and Allied Health Literature (CINAHL), focusing on studies involving healthcare professionals such as paramedics, nurses, and emergency medical technicians (EMTs). The review included randomized controlled trials (RCTs), clinical trials, and cohort studies that assessed various training modalities like virtual reality (VR) simulations, case-based learning (CBL), and hands-on workshops. Quality assessment was performed using the Cochrane risk-of-bias (RoB) tool for randomized trials and the Newcastle-Ottawa Scale (NOS) for clinical trials, ensuring methodological rigor and consistency. The findings suggest that CBL significantly improves knowledge retention and prehospital primary survey skills, outperforming other methods such as simulation exercises, which showed mixed results. VR training increased confidence levels but did not demonstrate significant improvements in objective skills compared to traditional methods. The use of supplementary triage assistance teams (physician-nurse supplementary triage team (MDRNSTAT)) was found to be effective during high patient volume hours, though not cost-effective as a daytime strategy. While the review highlights the importance of interactive and scenario-based training programs, limitations such as variability in study designs, publication bias, and language bias were noted, suggesting that caution should be exercised in generalizing the results. Future research should focus on long-term effectiveness, the integration of emerging technologies, and larger, well-designed trials across diverse healthcare settings to strengthen the evidence base.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".