Adapting to the COVID-19 pandemic: A New Teaching Model of Self-Directed Learning for Medical Students (Preprint)
Bibliographic record
Abstract
During the COVID-19 lockdown, medical schools in the United Kingdom withdrew their students from clinical placements and delivered education and examinations via online platforms.The logistical difficulty of timetabling a multitude of clinicians, with many working busier rotas on the front lines, to give one-hour lectures delayed the delivery of medical education.During this delay, the United Hospitals (UH) Medgroup set up an online platform called TeachtoLondon that recruited doctors and senior students to deliver 10-minute tutorials.Even with medical school teaching having resumed, TeachtoLondon remains popular due to its efficient and bespoke model.The short tutorials made the content more accessible and, more importantly, more useful as a revision tool.Compared to a one-hour online lecture that lacks 'virtual bookmarks', a playlist of tutorials allows easy navigation for students to revisit difficult topics, a pivotal part of learning.Teachtolondon is also popular with tutors, as it facilitates participation in teaching despite reduced availability due to COVID redeployment.It also allowed recruitment of doctors internationally, who would have been prevented by time zone differences from giving live lectures.Lastly, the UH network allowed students from any of the London medical schools to request a tutorial topic.Topics were allocated to the large database of tutors, providing an efficient turnaround, which is flexibility that a medical school's rigid curriculum does not allow.The TeachtoLondon project could be adapted as an effective teaching model that promotes digestible, bite-sized learning and provides uniform teaching to students, whilst simultaneously acting as a revision tool.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".