Exposure and Reception of Surgery Live-Streaming Technology in Undergraduate Medical Education
Bibliographic record
Abstract
Virtual education has undergone rapid development during and since the COVID-19 pandemic. Surgical live-streaming is one such virtual format that can provide invaluable learning, particularly when limited by distance or contact restrictions. This study aimed to explore the current exposure level and reception of surgical live-stream opportunities in undergraduate medical students. An invitation to participate in an anonymous survey was sent to the McMaster University undergraduate medical education program’s class of 2026 (n = 221) of. The survey consisted of preliminary questions indicating the degree of exposure to livestreaming participation, and two different sets of questionnaires that were assigned depending on presence or absence of prior participation. 22 (10%) students participated in and completed the survey. 21 (95%) students indicated that they were unaware of any live streaming opportunities, and 22 (100%) students indicated absence of prior participation in virtual surgical livestreaming. Interest level in attending a live-stream event given the opportunity was “High” or “Very High” for 19 (86.4%) students. Such results show that while there currently is insufficient opportunity for medical students to participate in surgical live streaming, there is ample interest. Further, live streaming was preferred as an adjunct to, rather than a replacement for, in-person opportunities in 77.3% of students. We hope that the results of this study will be able to assist the educational planners and other stakeholders in medical education to consider the appropriate application of surgical live streaming in future curricula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".