Trauma Base Knowledge and the Effect of the Trauma Evaluation and Management Program among Senior Medical Students in Seven Countries
Bibliographic record
Abstract
BACKGROUND: We compared base trauma knowledge and the impact of the Trauma Evaluation and Management (TEAM) program among senior medical students in seven countries. METHODS: We compared pre- and post-TEAM multiple choice question scores of fourth-year students in Jamaica (n = 32), Trinidad (n = 32), Costa Rica (n = 64), Australia (n = 35), United Arab Emirates (n = 68), Toronto (n = 29) and Pennsylvania (n = 34). Means and degree of improvement were compared by analysis of variance (p < 0.05 for statistical significance). Percentage pass (based on 70% or 60% pass mark), student's perception of instruction level, and grading of TEAM (based on the percentage of students grading 1-5 for each category) were assessed by chi2 analysis. [table: see text]. RESULTS: Only 31.4% of students achieved the borderline pass mark of 60%, and 5.4% achieved a clear pass mark of 70%. The performance before and after TEAM was quite variable among medical schools. A grade of > or = 4 was assigned by 74% to 100% for objectives, knowledge improvement, satisfaction, and recommending TEAM for the curriculum. TEAM was rated "just right" by 70.3% to 92.7%, "too simple" by 1.6% to 21.6%, and "too advanced" by 3.3% to 13.5% of students. CONCLUSION: Base trauma knowledge in these students, though variable, was generally very low and improved with TEAM. Our data suggest a need for greater undergraduate emphasis in trauma education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".