From struggle to opportunity: Reimagining medical education in a pandemic era
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted the international medical education community in unprecedented ways. The restrictions imposed to control the spread of the virus have upended our routines and forced us to reimagine our work structures, educational programming and delivery of patient care in ways that will likely continue to change how we live and work for the foreseeable future. Yet, despite these interruptions, the pandemic has additionally sparked a transformative impulse in some to actively engage in critical introspection around the future of their work, compelling us to consider what changes could (and perhaps should) occur after the pandemic is over. Drawing on key concepts associated with scholar Paulo Freire's critical pedagogy, this paper serves as a call to action, illuminating the critical imaginings that have come out of this collective moment of struggle and instability, suggesting that we can perhaps create a more just, compassionate world even in the wake of extraordinary hardship.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.058 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".