Approaches and Methods of Teaching Head Trauma to Medical Students: A Scoping Review to Identify Gaps and Future Research Opportunities
Bibliographic record
Abstract
In the context of teaching head trauma to medical students, the existing literature indicates that traditional educational approaches, such as workshops and clinical exposure, enhance knowledge and diagnostic skills, but often face a lack of structured content in undergraduate curricula, such that 70% of Canadian medical schools lack sufficient content on concussion (Husain, 2024). However, recent studies emphasize the application of digital tools and gamification, where a digital game-based study guide increased nursing students' theoretical knowledge from an average of 2.5 to 5.59 and improved practical skills to 19.32 out of 20, indicating the potential of these tools for risk-free simulation of clinical scenarios (Bashiri Bonab, 2025). These programs are primarily focused on case studies, without comprehensive mapping of the status review of current educational approaches, gamified and digital tools, and learning outcomes. Accordingly, the present scoping review will be conducted with the aim of identifying and describing various current educational approaches for learning head trauma among students, gamified and digital tools, and learning outcomes, and consequently, systematically map the research conducted in this field and identify existing knowledge gaps.
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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 | 0.028 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.031 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".