If we could turn back time: Imagining time-variable, competency-based medical education in the context of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has exposed a paradox in historical models of medical education: organizations responsible for applying consistent standards for progression have needed to adapt to training environments marked by inconsistency and change. Although some institutions have maintained their traditional requirements, others have accelerated their programs to rush nearly graduated trainees to the front lines. One interpretation of the unplanned shortening of the duration of training programs during a crisis is that standards have been lowered. But it is also possible that these trainees were examined according to the same standards as usual and were judged to have already met them. This paper discusses the impacts of the COVID-19 pandemic on the current workforce, provides an analysis of how competency-based medical education (CBME) in the context of the pandemic might have mitigated wide-scale disruption, and identifies structural barriers to achieving an ideal state. The paper further calls upon universities, health centres, governments, certifying bodies, regulatory authorities, and health care professionals to work collectively on a truly time-variable model of CBME. The pandemic has made clear that time variability in medical education already exists and should be adopted widely and formally. If our systems today had used a framework of outcome competencies, sequenced progression, tailored learning, focused instruction, and programmatic assessment, we may have been even more nimble in changing our systems to care for our patients with COVID-19.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| 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".