Competency-based medical education: The spark to ignite healthcare’s escape fire
Bibliographic record
Abstract
High-value care is what patients deserve and what healthcare professionals should deliver. However, it is not what happens much of the time. Quality improvement master Dr. Don Berwick argued more than two decades ago that American healthcare needs an escape fire, which is a new way of seeing and acting in a crisis situation. While coined in the U.S. context, the analogy applies in other Western healthcare contexts as well. Therefore, in this paper, the authors revisit Berwick’s analogy, arguing that medical education can, and should, provide the spark for such an escape fire across the globe. They assert that medical education can achieve this by fully embracing competency-based medical education (CBME) as a way to place medicine’s focus on the patient. CBME targets training outcomes that prepare graduates to optimize patient care. The authors use the escape fire analogy to argue that medical educators must drop long-held approaches and tools; treat CBME implementation as an adaptive challenge rather than a technical fix; demand genuine, rich discussions and engagement about the path forward; and, above all, center the patient in all they do.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 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".