Virtual Surgical Shadowing for Undergraduate Medical Students: A Pilot Program
Bibliographic record
Abstract
Background: Due to the COVID-19 pandemic, inperson physician shadowing has been restricted at many medical schools throughout Canada. We sought to address this gap by introducing a novel virtual shadowing experience to expose medical students to surgical specialties, and to assess possible improvements in the quality of delivering medical education. Methods: In compliance with the Health Insurance Portability and Accountability Act, two cameras were placed in an operating room to stream surgical procedures live to medical students. A survey was then distributed after the shadowing experience. Results: Ten medical students attended the 2.5-hour virtual surgical shadowing experience and nine provided feedback through a survey. The survey consisted of six Likert scale questions and two short-answer questions. Participants scored an average of 4.6±0.52 for the technology being conducive to their learning; 4.7±0.50 that the session met their learning objectives; and 4.8±0.44 regarding the knowledge and skills gained being useful for clerkship. Areas of improvement included improved camera quality (n=3) and the provision of case information prior to the sessions (n=4). Discussion: The virtual surgical shadowing program enabled students to effectively and reliably observe surgical procedures in real time, whilst engaging and communicating with the surgeons. Encouraging survey responses demonstrated the positive potential for future iterations of similar observerships in other surgical specialties, and as a means of improved medical education. Conclusion: Virtual surgical shadowing is a promising and innovative solution to limitations of in-person observerships, providing a secure and accessible way for medical students to explore surgical specialties.
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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 | 0.001 | 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 | 0.000 |
| 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".