Battlefield Trauma Training: A Pilot Study Comparing the Effects of Live Tissue vs. High-Fidelity Patient Simulator on Stress, Cognitive Function, and Performance
Bibliographic record
Abstract
Within the Canadian Armed Forces (CAF), the Tactical Medicine (TACMED) course is used to train medical technicians (i.e., medics) in battlefield trauma care. Although training is administered using both simulators (SIM) and live tissue (LT), little is known about their relative effects on stress and cognitive function in this context. To address this shortcoming, we conducted a pilot study and collected self-report (State-Trait Anxiety Inventory [STAI]) and biological measures of stress (salivary cortisol and dehydroepiandrosterone [DHEA]), as well as working memory (WM) and short-term memory (STM) data from medics (N = 20) assigned randomly to training and skill assessment using either SIM or LT. Skill assessment resulted in the elevation of STAI scores and salivary cortisol and DHEA levels. WM and STM performance were better at the time of skill assessment, and WM performance exhibited a positive correlation with salivary cortisol level. Salivary cortisol and DHEA levels, STAI scores, and memory performance did not predict pass/fail rates on combat casualty care skills. Although the TACMED course was associated with elevated stress and improved memory performance, those effects were not affected by the training modality. We end by discussing lessons learned from our pilot study and highlight outstanding questions that remain to be addressed in future studies on this topic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".