Bibliographic record
Abstract
The study of medical education has broadened significantly over the past decade to include a wide variety of theoretical frameworks from multiple research domains. There remains a significant misconception, however, that learning theories (largely drawn from cognitive psychology and education) are practical and useful to educators, whereas other types of theory are not. The authors of this commentary reflect on a learning-theory-based model for developing master learners presented by Schumacher and colleagues in this issue of Academic Medicine. They suggest that bioscientific and sociocultural theories can enhance different aspects of that model and provide specific examples from neuropsychophysiology, Foucauldian discourse analysis, and critical theory. Bioscientific and sociocultural theories such as these present medical educators with an exciting array of new methodological and interpretive possibilities. The authors illustrate ways in which these theories can have important practical applications for, and impacts on, the practice of medical education.
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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 | 0.074 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.025 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".