The Simulated Trauma Patient Teaching Module—Does it Improve Student Performance?
Bibliographic record
Abstract
BACKGROUND: Student feedback from the old TEAM (Trauma Evaluation and Management) program prompted introduction of simulated trauma patient models in the new program. Performance after the new and old programs was compared to assess the impact of the simulated patient models. METHODS: Final year medical students randomly assigned to control and experimental groups completed a 20-item trauma multiple choice questionnaire examination (MCQE). The experimental groups attended the old or new TEAM program before completing a second MCQE and the control groups completed the same post-test without the TEAM programs. We used paired t tests for within and unpaired t tests for between group comparisons of the control and experimental groups' performances on the MCQ pre- and post-tests. On a 1 to 5 scale, students graded if objectives were met; trauma knowledge improved; trauma skills improved; overall satisfaction; and if TEAM should be mandatory. RESULTS: Post-test scores increased significantly after both the old and new programs but the increase was statistically significantly greater after the new program. In the old TEAM, 51.6% rated improvement in trauma skills at 4 or greater compared with 97.3% in the new program. A large percentage of students in the old program requested more hands-on teaching. Of students, 85% scored honors pass mark after completion of the new TEAM format, and no honors pass marks were achieved after completion of the old TEAM format. CONCLUSION: Simulated trauma patient models were rated highly and improved both trauma skills and knowledge. Wider application of these teaching models is suggested.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".