Defining Medical Education Scholarship
Bibliographic record
Abstract
Understanding the definition of medical education scholarship and its underlying theoretical constructs is essential to constructing medical education and supporting learning across the continuum. There continues to exist uncertainty surrounding defining and enacting medical education scholarship. This uncertainty results in an inability for educators to conduct medical education and scholarship and may result in missed opportunities for educators and learners across the continuum. For this paper, the authors define medical education scholarship as endeavours that are purposefully undertaken and may surpass the borders of those traditionally defined as research or innovation. Medical education may take place in areas of discovery, integration, application, teaching, and engagement. This definition is based on works by Boyer, Glassick and Shulman. This paper describes the contribution that each of these seminal works has made to advance the definition of medical education scholarship. The differences between medical education scholarship and creative professional activities as well as daily work are explored throughout the paper. The paper concludes with a call to develop, demonstrate, promote and support medical education scholarship and the faculty who are focused on undertaking related activities.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".