Impact of COVID-19 on medical education: introducing homo digitalis
Bibliographic record
Abstract
PURPOSE: To determine how members of the Société Internationale d'Urologie (SIU) are continuing their education in the time of COVID-19. METHODS: A survey was disseminated amongst SIU members worldwide by email. Results were analyzed to examine the influence of age, practice region and settings on continuing medical education (CME) of the respondents. RESULTS: In total, 2494 respondents completed the survey. Internet searching was the most common method of CME (76%; all ps < 0.001), followed by searching journals and textbook including the online versions (62%; all ps < 0.001). Overall, 6% of the respondents reported no time/interest for CME during the pandemic. Although most urologists report using only one platform for their CME (26.6%), the majority reported using ≥ 2 platforms, with approximately 10% of the respondents using up to 5 different platforms. Urologists < 40 years old were more likely to use online literature (69%), podcasts/AV media (38%), online CME courses/webinars (40%), and social media (39%). There were regional variations in the CME modality used but no significant difference in the number of methods by region. There was no significant difference in responses between urologists in academic/public hospitals or private practice. CONCLUSION: During COVID-19, urologists have used web-based learning for their CME. Internet learning and literature were the top frequently cited learning methods. Younger urologists are more likely to use all forms of digital learning methods, while older urologists prefer fewer methods.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".