Stopping the Bleed: Can Just-in-Time Training Improve the Tourniquet Application Competencies of Bystanders and First Responders? – A Randomized Control Trial
Bibliographic record
Abstract
Background/Introduction: Public education in effective interventions for external hemorrhage control for gunshot wounds has become a priority focus. There remains a gap in training programs of populations of non-medical bystanders who would be the first to stop this bleeding in a mass shooting. Objectives: To create a WHO EMT initiative community training program designed to close this gap in low-middle income countries, complex humanitarian events and in conflict zones. Method/Description: This study is a factorial randomized control study, utilizing four cohort groups. Two comprised of bystanders with no previous medical training, and the remaining two comprised of first responders previously trained to control external hemorrhage. Each group was put through the same hemorrhage control simulation; one cohort of each bystander/first responder groups acted as a respective control group receiving only a tourniquet, whereas other cohorts of each group received Stop-the-Bleed® handouts to serve as the point-of-care instructional method of Just-in-Time training alongside the tourniquets. Results/Outcomes: Within the bystander’s cohort, 26.3% of the group who received JiT training applied the tourniquet correctly vs 6.3% of the control group. Of the first responder’s cohort, 75% of those who received JiT training applied the tourniquet correctly vs 66.7% of the control group. There was no statistically significant difference in the ability to correctly apply the tourniquet in the intervention vs control groups of either cohort. Conclusion: The WHO EMT initiative has the opportunity to train non-medical bystanders to receive Just-in-Time training to effectively place a tourniquet to stop the bleeding after a mass shooting.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".