Enacting Pedagogy in Curricula: On the Vital Role of Governance in Medical Education
Bibliographic record
Abstract
Managing curricula and curricular change involves both a complex set of decisions and effective enactment of those decisions. The means by which decisions are made, implemented, and monitored constitute the governance of a program. Thus, effective academic governance is critical to effective curriculum delivery. Medical educators and medical education researchers have been invested heavily in issues of educational content, pedagogy, and design. However, relatively little consideration has been paid to the governance processes that ensure fidelity of implementation and ongoing refinements that will bring curricular practices increasingly in line with the pedagogical intent. In this article, the authors reflect on the importance of governance in medical schools and argue that, in an age of rapid advances in knowledge and medical practices, educational renewal will be inhibited if discussions of content and pedagogy are not complemented by consideration of a governance framework capable of enabling change. They explore the unique properties of medical curricula that complicate academic governance, review the definition and properties of good governance, offer mechanisms to evaluate the extent to which governance is operating effectively within a medical program, and put forward a potential research agenda for increasing the collective understanding of effective governance in medical education.
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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".