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Record W4404568580 · doi:10.7759/cureus.74089

Evaluating the Effectiveness of Trauma Care and Emergency Preparedness Training Programs on Prehospital Primary Survey Skills: A Systematic Review

2024· review· en· W4404568580 on OpenAlexaboutno aff
Ayesha Sajid, Manahil Awan, FNU Warsha, Shahzad Ahmad, Lara Alsadoun

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriageCochrane LibraryPreparednessCINAHLRandomized controlled trialHealth careMEDLINEModalitiesNursingMedical emergencyPsychological intervention

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.066
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.159
GPT teacher head0.454
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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