Roadmap for creating an accelerated three-year medical education program
Bibliographic record
Abstract
Medical education is undergoing significant transformation. Many medical schools are moving away from the concept of seat time to competency-based education and introducing flexibility in the curriculum that allows individualization. In response to rising student debt and the anticipated physician shortage, 35% of US medical schools are considering the development of accelerated pathways. The roadmap described in this paper is grounded in the experiences of the Consortium of Accelerated Medical Pathway Programs (CAMPP) members in the development, implementation, and evaluation of one type of accelerated pathway: the three-year MD program. Strategies include developing a mission that guides curricular development - meeting regulatory requirements, attaining institutional buy-in and resources necessary to support the programs, including student assessment and mentoring - and program evaluation. Accelerated programs offer opportunities to innovate and integrate a mission benefitting students and the public. ABBREVIATIONS: CAMPP: Consortium of accelerated medical pathway programs; GME: Graduate medical education; LCME: Liaison committee on medical education; NRMP: National residency matching program; UME: Undergraduate medical education.
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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".