Sacrifice as a Part of Medical Education: A Reflection on the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic demanded significant sacrifices from medical learners. We examine the meaning of sacrifice and frame it as a "side effect" of being dedicated to the good of the patient. We contend that sacrifice has played a central role in medicine, even before the pandemic, for professionals and learners alike. We identify three limits to the role of sacrifice in medical education and practice to separate healthy from harmful experiences of sacrifice. Developing an understanding of sacrifice in medical education and practice can help trainees and clinicians know when to marshal resilient responses to healthy sacrifices and reject harmful sacrifices encountered. Maintaining this balance requires a broader reflection on the nature of medical schools and their ability to support virtuous professional identity formation.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.053 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.014 | 0.040 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".