Improving Hands-On Suture Opportunities for Medical Students: A Collaborative Initiative Between Medical Students and a Simulation Lab
Bibliographic record
Abstract
Technical skills are an integral part of the practice of medicine. Simulation-based education (SBE) is a widely employed approach that allows students to acquire these skills prior to practicing them in the clinical setting. To discuss the state of SBE and potential avenues to improving education and medical student experiences, this editorial will explore the lived experiences of junior medical students, the observations of a research graduate student's informal conversations, and an educational quality improvement (EQI) pilot conducted by students at a satellite medical campus. Pre-clerkship Canadian medical students reported having limited opportunities to practice their technical skills. For some, these SBE sessions came at inopportune times in their academic journey, preventing them from maximizing their chances at real-world exposure. Having identified this as an issue, students sought ways to allow themselves and their peers to practice technical skills outside of the undergraduate medical curriculum, such as organizing peer and near-peer-led suturing events. Still, students feel these sessions are a start but do not adequately meet their needs, as access to practice materials is still restricted to the sparse events held by students, and experienced feedback is scant. To address these needs, we explore how simulation technology research and development labs can support peer-assisted learning by training students to teach technical skills and provide feedback to their peers. We also propose increasing access to simulation materials asynchronously to allow for practice when the students can benefit most.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".