Developing the experts we need: Fostering adaptive expertise through education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this era of increasing complexity, there is a growing gap between what we need our medical experts to do and the training we provide them. While medical education has a long history of being guided by theories of expertise to inform curriculum design and implementation, the theories that currently underpin our educational programs do not account for the expertise necessary for excellence in the changing health care context. The more comprehensive view of expertise gained by research on both clinical reasoning and adaptive expertise provides a useful framing for re-shaping physician education, placing emphasis on the training of clinicians who will be adaptive experts. That is, have both the ability to apply their extensive knowledge base as well as create new knowledge as dictated by patient needs and context. Three key educational approaches have been shown to foster the development of adaptive expertise: learning that emphasizes understanding, providing students with opportunities to embrace struggle and discovery in their learning, and maximizing variation in the teaching of clinical concepts. There is solid evidence that a commitment to these educational approaches can help medical educators to set trainees on the path towards adaptive expertise.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.459 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it