Bibliographic record
Abstract
For all its traditional successes, the current model of medical education in the United States and Canada is being challenged on issues of quality, throughput, and cost, a process that has exposed numerous shortcomings in its efforts to meet the needs of the nations' health care systems. A radical change in direction is required because the current path will not lead to a solution.The 2010 publication Educating Physicians: A Call for Reform of Medical School and Residency identifies several goals for improving the medical education system, and proposals have been made to reform medical education to meet these goals. Enacting these recommendations practically and efficiently, while training more health care providers at a lower cost, is challenging.To advance solutions, the authors review innovations that are disrupting higher education and describe a vision for using these to create a new model for competency-based, learner-centered medical education that can better meet the needs of the health care system while adhering to the spirit of the above proposals. These innovations include collaboration amongst medical schools to develop massive open online courses for didactic content; faculty working in small groups to leverage this online content in a "flipped-classroom" model; and digital badges for credentialing entrustable professional activities over the continuum of learning.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.173 | 0.100 |
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".