Reconsidering “good teaching” across the continuum of medical education
Bibliographic record
Abstract
There is no shortage of sustained inquiry into the nature and evaluation of teaching in medical education. For the most part, however, this growing and respectable body of inquiry has uncritically adopted a single model of effective teaching that is assumed to be appropriate across variations in context, learners, and teachers. This article presents five alternative views of "good teaching" and challenges the trend toward any single, dominant view of what constitutes good teaching. Based on 10 years of research, in five different countries, studying hundreds of educators in adult and higher education across a wide range of disciplines, contexts, and cultures, we have evidence of five different perspectives on good teaching: transmission, developmental, apprenticeship, nurturing, and social reform. Each perspective represents a philosophical orientation to knowledge, learning, and the role and responsibility of being an educator. A "snapshot" of each perspective is provided, including an example from continuing medical education (CME), a set of key beliefs, primary responsibilities, typical strategies, and common difficulties. Readers are encouraged to use the five perspectives as a means of identifying, articulating, and revisiting assumptions and beliefs they hold regarding their view of effective teaching. They are also encouraged to resist a "one-size-fits-all" approach to the investigation, improvement, or evaluation of teaching in CME.
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".