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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".