Bibliographic record
Abstract
Since 2000, the Association of American Medical Colleges (AAMC) has published “A Snapshot of Medical Student Education in the United States and Canada” every 10 years as a supplement to Academic Medicine. The undergraduate medical curricula and related features of nearly 150 accredited medical schools are outlined in the current issue. These synopses, taken together, provide a panoramic perspective on the state of undergraduate medical education in 2 North American countries just prior to the coronavirus pandemic in 2020. Individual entries in this supplement are brief but rich. Authored by education leaders at each school, the descriptions focus on curricular structure, governance, highlights, and innovations, as well as changes over the prior decade. Features such as class size, facilities, and staff support are detailed. The collection allows the reader to compare across schools and also to discern the unique configuration of mission, resources, geography, and educational approaches of each institution. The snapshot supplement represents a resource for leaders in academic medicine. Documenting core features and trends, the collection illustrates the current context of medical student education and signals where the health professions are headed. Creative new approaches to teaching, mentoring, assessment, inclusion and diversity, professional development, and well-being, along with traditional curricular elements, are shared throughout the snapshot supplement. The array of examples should stimulate careful consideration of the intentions and effectiveness of both accepted and novel educational practices. The idea for collecting curriculum updates from medical schools was originally brought to life years ago by Brownie Anderson,1 and the continued production of this supplement is the direct result of great efforts by Kate McOwen, Anne Farmakidis, and colleagues in Medical Education at the AAMC. As in the past, the snapshot supplement has value because it is comprehensive and accessible, it allows for comparisons across schools, and it reveals patterns and trends in undergraduate medical education.2 In our unrelenting efforts to strengthen the educational experiences of future physicians, the collection also provides an opportunity for leaders to reflect on institutional values and, very importantly, how they are expressed in culture and in curriculum.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.217 | 0.189 |
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".