Massive Hemorrhage Protocol Application and Teamwork Skills
Bibliographic record
Abstract
Abstract Objective Massive hemorrhages (MHs) are rare but serious complications of pediatric trauma and obstetric cases. This study aimed to evaluate the impact of interprofessional simulation to improve adherence to a MH protocol (MHP), teamwork skills and confidence levels during a hemorrhagic crisis situation. Methods This was a pre‐post experimental study conducted at a tertiary care mother–child simulation center. Pediatric emergency and obstetric teams were submitted to simulated trauma and postpartum MH scenarios. Training consisted of two case scenarios followed by debriefing sessions and a lecture on the MHP. The primary outcome was adherence to MHP processes (checklist) measured prior to and 2 weeks following training sessions. Other outcomes were the measure of teamwork skills (Mayo High Performance Teamwork Scale) and confidence of the participants. Results Sixty‐two health care professionals were involved in eight interprofessional teams. Mean scores for adherence to the MHP improved from 19.1 in the pretraining phase to 25.8 in the posttraining phase (difference of 6.7; 95% confidence interval [CI] = 4.4 to 8.9). Mean scores pertaining to teamwork skills also improved significantly between pre‐ and posttraining phases (difference = 3.9; 95% CI = 1.5 to 6.4). Confidence questionnaires showed significant improvements in the posttraining phase (difference = 6.9; 95% CI = 5.3 to 8.3). Conclusions Targeted training involving simulation and protocol review improved participant adherence to MHP processes and teamwork skills. Confidence levels improved across all disciplines.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".