The impact of COVID-19 on Medical education and Medical Students. How and when can they return to placements?
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. The defining feature of 2020 will be the early and mid-stages of the covid-19 pandemic, declared by the World Health Organisation on 11 th March. Rapid worldwide exponential spread continues and by 15 April, more than 1 900 000 cases and 123 000 deaths had been reported worldwide (WHO, 2020).Health services have coped to varying degrees. One common feature has been the withdrawal of routine care (Iacobucci, 2020a) and 'non-essential' staff including learners, although many have returned to undertake care roles. As the likely timeframe for stabilisation of health services becomes clearer, certainly in the United Kingdom (UK) (Iacobucci, 2020b), medical educators need to rapidly get the teaching of the next generation of health care workers back on track if they are to enter health services as confident and competent practitioners in 2020 and 2021.Although a 'whole world' experience, the effects of covid-19 sit in national contexts. We detail the issues for the UK in re-starting and re-inventing medical education, noting that the principles, if not necessarily the detail, will be common across the world.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.122 | 0.026 |
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".