Evaluation of “Stop the Bleed” training among K‐12 faculty and staff in Alabama
Bibliographic record
Abstract
OBJECTIVES: To evaluate "Stop the Bleed" (STB) training among/K12 personnel in an Alabama school system, and to assess participants' perceived readiness to train peers in STB methods. DESIGN AND SAMPLE: We performed a cross-sectional observational study with a convenience, nonprobability sample of 466 full-time personnel who received STB training. Data were collected using an anonymous online survey. MEASUREMENTS: We asked participants to recall feelings related to STB both prior to and after completing training using a 5-point Likert scale (5 = "Strongly Disagree", 1 = "Strongly Agree"). We used logistic regression to evaluate the association among posttraining feelings and perceived preparedness to train others in STB. RESULTS: Participants were primarily female (78%), aged 41 ± 10 years, who held faculty positions (94%). Results revealed increased knowledge of (4 [IQR 2-4] vs. 2 [1-2], p < .001) and comfort with (4 [2-5] vs. 2 [1-2], p < .001) STB skills. Participants felt more empowered to organize STB training (4 [3-5] vs. 3 [2-4], p < .001); those who felt empowered to organize STB training were eight times more likely to feel capable of teaching STB. CONCLUSIONS: After STB training, K-12 personnel felt empowered to organize additional STB trainings and capable of teaching STB methods to others.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".