Virtual learning in global surgery: current strategies and adaptation for the COVID-19 pandemic
Bibliographic record
Abstract
Modern surgical education has shifted to include technology as an integral component of training programs. The onset of the COVID-19 pandemic highlights the need to identify currently training modalities in global surgery and to delineate how these can be best used given the shift of global surgical training to the virtual setting. Here, we conducted a rapid review of the MEDLINE database examining the current status of training modalities in global surgical training programs and presented a case study of a virtual learning course on providing safe surgical care in the time of a pandemic. Our rapid review identified 285 publications, of which 101 were included in our analysis. Most articles describe training in high income country environments (87%, 88/101). The principal training modality described is apprenticeship (46%, 46/101), followed by simulation training (37%, 37/101), and virtual learning strategies (14%, 14/101). Our focused case study describes a virtual course entitled “Safe Surgical Care: Strategies During Pandemics,” created at the University of British Columbia by E.J., published 1-month postdeclaration of the pandemic. This multimodal course was rolled-out over a 5-week period and had significant engagement on an international level, with 1944 participants from 105 countries. With in-person training decreased as a result of the pandemic, virtual reality, virtual simulation, and telementoring may serve to bridge this gap. We propose that virtual learning strategies be integrated into global surgical training through the pursuit of increased accessibility, incorporation of telementoring, and inclusion in national health policy.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".