Teaching Effectiveness of the Trauma Evaluation and Management Module for Senior Medical Students
Bibliographic record
Abstract
BACKGROUND: The Trauma Evaluation and Management (TEAM) module was devised by the American College of Surgeons for teaching senior medical students trauma management principles. This article reports on the teaching effectiveness of this module. METHOD: Cognitive skills (by 20 item multiple-choice question examination on trauma topics) and clinical trauma management skills performance, using the Objective Structured Clinical Examination, were compared between two groups of 16 randomly selected final year medical students who had completed the standard curriculum including trauma topics. One group had the TEAM (TEAM group) and the other did not (no-TEAM group). Objective Structured Clinical Examination score (percentage), Priority score (range, 1-7), Organized Approach score (range, 1-5), and Global Pass status were assigned at each station. The students also completed a five-part questionnaire. RESULTS: Results of the questionnaire showed that on a scale of 1 to 5, with 5 being excellent, 96.8% assigned a score of 4 or greater, indicating the objectives were met, 83.8% that trauma knowledge was improved, 51.6% that clinical skills were improved, 90.3% that the module should be mandatory, and 83.9% overall satisfaction with the program. CONCLUSION: The TEAM module is very effective in teaching trauma management principles to senior medical students, by whom the program was very well received. Consideration should be given to adopting this program more widely in our medical undergraduate curriculum.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".