The new normal: Medical education during and beyond the COVID-19 pandemic
Bibliographic record
Abstract
The "new normal" is a trite axiom that has permeated every sphere impacted by the COVID-19 pandemic, within and beyond healthcare.Healthcare has quickly adopted necessary changes in the delivery of care, using virtual clinical encounters, recruiting an expanded workforce, disrupting longstanding hospital processes, and embracing the palpable existential angst associated with this "new normal."Medical learners have also been significantly impacted at this time.The experiences of medical students and residents across the globe generally vary based on clinical, regional, and personnel needs.Medical schools and residency programs have had to quickly respond and adapt to the spread of the pandemic by making rapid decisions with the best interests of faculty, staff, learners, and the public in mind.End-users, such as learners, appear to have been excluded from these decision-making processes.In response to the pandemic, medical schools and residency programs have removed medical students from the clinical environment, redistributed residents throughout the health system, and moved learning and assessments to online platforms.This represents a substantial disruption to the status quo of medical education.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".