Bibliographic record
Abstract
One hundred years after Abraham Flexner released his report Medical Education in the United States and Canada, the spirit of reform is alive again. Reports in the United States and Canada have called for significant changes to medical education that will allow doctors to adapt to complex environments, work in teams, and meet a wide range of social needs. These reports call for clear educational outcomes but also for a flexible, individualized approach to learning. Whether or not change will result has much to do with the alignment between what is proposed and the nature of current societal discourses about how medical education should be conducted. Currently, two powerful and competing models of competence development are operating at odds with one another. The traditional one is time-based (a "tea-steeping" model, in which the student "steeps" in an educational program for a historically determined fixed time period to become a successful practitioner). This model directs attention to processes such as admission and curriculum design. The newer one is outcomes-based (an "i-Doc" model, a name suggested by the Apple i-Pod that infers that medical schools and residencies, like factories, can produce highly desirable products adapted to user needs and desires). This model focuses more on the functional capabilities of the end product (the graduate student, resident, or practicing physician). The author explores the implications of both time-based and outcomes-based models for medical education reform and proposes an integration of their best features.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".