Simulation and Active Learning Decreases Training Time of an Emergency Triage Assessment and Treatment Course in Pilot Study in Malawi
Bibliographic record
Abstract
Objectives The aim of the Emergency Triage Assessment and Treatment (ETAT) plus trauma course is to improve the quality of care provided to infants and children younger than 5 years. The curriculum was revised and shortened from 5 to 2.5 days by enhancing simulation and active learning opportunities. The aim of this study was to examine the feasibility and value of the new short-form ETAT course by assessing postcourse knowledge and satisfaction. Methods We delivered the short-form ETAT course to a group of interdisciplinary health workers in Malawi. Precourse and postcourse knowledge was assessed using a standardized 20 questions short answer test used previously in the 5-day courses. A 13-statement survey with 2 open-ended questions was used to examine participant satisfaction. Results Participants' postcourse knowledge improved significantly (P < 0.001) after the shorter ETAT course. Participants reported high levels of satisfaction with the short-form ETAT. Conclusions Simulation and other active learning strategies reduced training time by 50% in the short-form ETAT course. Participants with and without previous ETAT training improved their knowledge after participating in the short-form ETAT course. Reduced training time is beneficial in settings already burdened by scarce human resources, may facilitate better access to in-service training, and build capacity while conserving resources in low-resource settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".