TP8.2.5 Taking the virtual plunge: delivering an intensive curriculum at the 'St Thomas' MRCS course' in the COVID era
Bibliographic record
Abstract
Abstract Aims The Covid-19 global pandemic changed the world. Disruption to teaching and training has been felt across medicine, but more acutely in craft specialities such as surgery. The Royal College of Surgeons has raised concerns and started a campaign: ‘no training today, no surgeons tomorrow’. Innovation and adaptation are required in this new normal. We assess the effectiveness of adapting an intensive face-to-face revision course covering essential skills and knowledge required for the Membership of the Royal College of Surgeons (MRCS) examination to the virtual world. Methods Over five days interactive lectures, small-group teaching (clinical examination, communication, procedural skills), and a complete mock examination were delivered by a faculty of expert lecturers, consultants and actors live over Zoom. Feedback was collected on all aspects of the course by online survey. Sessions were marked for presentation, clarity, relevance and overall quality. Results 19 participants attended 35 sessions and six mock stations, with a total of 597 candidate sessions and 108 candidate mock stations. 94% of ratings were at least very good; 63% were excellent. Participants reported significantly improved levels of skill and knowledge (p < 0.001). Most felt skills improved from fair to very good. All candidates felt the course was well organised and allowed full participation. Conclusion Increasingly, medical education is occurring in the virtual world. Whilst this poses difficulty in craft specialities, particularly for skill acquisition, our data demonstrate high participant satisfaction. Moreover, significant improvements were seen in self-assessment of skills and knowledge as a consequence of this unique course.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".