Assessing the Impact of the Trauma Team Training Program in Tanzania
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa, injury is responsible for more deaths and disability-adjusted life years than AIDS and malaria combined. The trauma team training (TTT) program is a low-cost course designed to teach a multidisciplinary team approach to trauma evaluation and resuscitation. The purpose of this study was to assess the impact of TTT on trauma knowledge and performance of Tanzanian physicians and nurses; and to demonstrate the validity of a questionnaire assessing trauma knowledge. METHODS: This is a prospective study of physicians and nurses from Dar es Salaam undergoing TTT (n = 20). Subjects received a precourse test and, after the course, an alternate postcourse test. The equivalence and construct validity of these 15-item multiple-choice questionnaires was previously demonstrated. After the course, subjects were divided into four teams and underwent a multiple injuries simulation, which was scored with a trauma resuscitation simulation assessment checklist. A satisfaction questionnaire was then administered. Test data are expressed as median score (interquartile ratio) and were analyzed with the Wilcoxon's signed rank test. RESULTS: After the TTT course, subjects improved their scores from 9 (5-12) to 13 (9-13), p = 0.0004. Team performance scores for the simulation were all >80%. Seventy-five percent of subjects were very satisfied with TTT and 90% would strongly recommend it to others and would agree to teach future courses. CONCLUSIONS: After completion of TTT, there was a significant improvement in trauma resuscitation knowledge, based on results from a validated questionnaire. Trauma team performance was excellent when assessed with a novel trauma simulation assessment tool. Participants were very supportive of the course.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".