Bibliographic record
Abstract
The specialty of research in medical education began just over three decades ago with a small group of clinicians and educational researchers at the medical school in Buffalo, New York.Since that time it has expanded worldwide.This paper is a personal reflection on how this research has informed our understanding of learning, teaching, and assessment in medicine. Measuring progressIn medicine, indicators of scientific progress might be measured by objective indicators such as death from cardiovascular disease.In education such "hard" evidence may be lacking for several reasons.Firstly, paradoxically, real differences in educational strategies may not be reflected in outcomes, such as licensing examination performance, simply because students are highly motivated and are not blinded to the intervention, so will compensate for any defects in the curriculum.[1][2][3][4][5] Secondly, a curriculum is not like a drug, which can be given at standard doses, but instead contains many components, delivered with variable quality by different teachers.Finally, the time between learning and important outcomes may be so long that the effects of the curriculum are obscuredalthough not always.6 Summary pointsResearch in medical education has contributed substantially to understanding the learning processThe educational community is becoming aware of the importance of evidence in educational decision making Areas of major development include basic research on the nature of medical expertise, problem based learning, performance assessment, and continuing education and assessment of practising physicians
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 | 0.147 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".