Effect of the Trauma Evaluation and Management module on the knowledge of senior medical students: a prospective cohort study
Bibliographic record
Abstract
Background: Despite the high incidence of motor vehicle collisions and associated mortality rates in Saudi Arabia, formal trauma training and management for undergraduate medical students is not optimal. The aim of our study was to assess the effect of the Trauma Evaluation and Management (TEAM) module on trauma knowledge among senior medical students. Methods: Final-year medical students were recruited between September 2016 and May 2017 at King Abdulaziz University, Jeddah. They were allocated to 1 of 2 groups: 1 group was exposed to the TEAM module, and the other was not (control group). We employed a widely used 20-item multiple-choice standardized questionnaire to assess trauma-related knowledge of both groups. Results: Our study included 136 participants, 68 in the TEAM module group and 68 in the control group. The mean scores for trauma-related knowledge were 68.4% (standard deviation [SD] 15.63%) and 45.4% (SD 19.52%), respectively. Linear regression analysis showed that the TEAM module participants scored 23% higher on the test than the control participants (β = 22.94%, 95% confidence interval 16.94%–28.94%). Conclusion: Mean test scores were significantly higher for those who completed the TEAM module than for those who did not. We highly recommend incorporating the TEAM module into the formal medical curriculum at all Saudi universities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".