Bibliographic record
Abstract
CONTEXT: The author describes a career in which he combined clinical surgery with the formal study of medical education. In the 1980s, when the author embarked on this career track, it was an uncommon pathway. Over the last 30 years there has been an exponential increase in the number of individuals who have made medical education their principal academic focus. This paper provides examples from the author's personal story and lessons derived from that experience. PROCESS: The author outlines his experience of attaining formal training in education and concludes that this training was a foundational element in his pursuit of a career in health education research. The author describes his involvement in the transition from paper and pencil-based tests to performance-based testing in high-stakes examinations. He describes the development of a research centre in health professions education and the establishment of a simulation centre. The author's experiences in the development of an examination intended to measure technical skills, in the adoption of surgical safety checklists and in the elaboration of a programme in competency-based education are discussed. DISCUSSION: The author describes several of the lessons learned in the course of his career in medical education. He argues that successful enterprises in scholarship in medicine are almost invariably the product of interdisciplinarity. He describes the power of a joint venture between a university and an academic hospital. He argues that the geographical footprint of an emerging centre is critical. He discusses the importance of graduate studentship in an emerging discipline and enterprise.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".