The role of educational theory in continuing medical education: Has it helped us?
Bibliographic record
Abstract
Despite the existence of many approaches to understanding learning and change and attempts to incorporate these into continuing education research and practice, the search continues for a comprehensive understanding of how learning is engendered in professional practice and the processes by which learning and change occur. This article considers four broad questions in relation to the practice of continuing education: (1) What can be expected of theory? (2) How does theory relate to the educational practice of those in continuing education and the goals of continuing medical education ? (3) How have practice and theory mutually informed our current understandings? (4) How can theory serve the field more effectively in the future? Broad orientations to understanding learning provide a framework for examining the contributions of theory and practice. The orientations include behaviorist, cognitivist, social learning, humanist, and constructivist; for each, an example is presented. Newer understandings also are introduced. The article concludes by considering reasons as to why theory appears not to have served us better and by offering ways in which those in continuing education can ensure greater usefulness of theory while contributing to its continued development.
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 | 0.085 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".