Vascular e-Learning in the MENA Region during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: With the steady rise in interest in e-learning and the sudden boost provoked by the COVID-19 pandemic, it becomes necessary to explore the e-learning experience within the medical community in the MENA region. Methods: An online survey was conducted during the early phase of the COVID-19 pandemic (June 15 – October 15, 2020). Results: Seventy-eight vascular surgeons and trainees from 16 countries participated. 88% of the participants were male. 55% attended more than 4 activities. More than half of the activities did not lead to any official certification. Topic was the primary determinant for attending an activity. National societies and social media played a major role in disseminating activity-related information. Lack of time, increased workload, differences in time zone, and technical issues were the main obstacles cited. 84.7% of the participants had a positive impression. Conclusion: As the COVID-19 pandemic boosted e-learning activities in vascular surgery, a shift was observed in the learning mode and new leadership skills were called upon. Novel ways of quality control are required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".