Bibliographic record
Abstract
The climate of academic medicine today was shaped in part by Abraham Flexner's recommendations in 1910's Medical Education in the United States and Canada. At the celebration of the 100th anniversary of the Flexner Report, however, some wonder whether the times require another look at our complex system of medical education. In fact, an underlying theme of many articles in this special issue of Academic Medicine is that the medical education community's response to the Flexner Report—and the individualistic, expert-centric culture to which it gave rise—may now work against the collaboration needed for greater integration across the medical education continuum, highly networked teams in discovery research, and interprofessionalism in clinical care. The question, as many authors suggest, is not whether medical education is being true to Flexner, but whether academic medicine is responding to the implications of post-Flexnerian education and whether it is able to embrace the cultural change needed to address 21st-century health care needs. This commentary examines this cultural shift and identifies some key trends behind it, concluding by suggesting five success factors for achieving transformational change, including ways the Association of American Medical Colleges is working to support its members in these efforts.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.043 | 0.043 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".