Reimagining medical education for primary care in the time of COVID-19: a world view
Bibliographic record
Abstract
This article sets out to highlight the challenges and opportunities for medical education in primary care realised during the COVID-19 pandemic and now being enacted globally. The themes were originally presented during a webinar involving educationalists from around the world and are subsequently discussed by members of the WONCA working party for education. The article recognises the importance of utilising diversity, addressing inequity and responding to the priority health needs of the community through socially accountable practice. The well-being of educators and learners is identified as priority in response to the ongoing global pandemic. Finally, we imagine a new era for medical education drawing on global connection and shared resources to create a strong community of practice.
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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 | 0.021 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".