Principles for Designing a Program for the Teaching and Learning of Professionalism at the Undergraduate Level
Bibliographic record
Abstract
Until recent years, the subject of professionalism was not addressed formally in the medical curriculum. Students became professionals without being aware of it, with the assumption being that they patterned their behavior on that of respected role models. It was only when both society and the profession came to believe that medicine's professionalism had been eroded by forces arising both inside and outside of the medical profession that it was deemed necessary to teach professionalism as a distinct subject, something that is now required by accrediting and certifying bodies. Without question professionalism can be taught and learned in many different educational settings, using a variety of pedagogic tools and methods. However, as faculties of medicine have gained experience in teaching professionalism, common threads have emerged. It has become possible to outline a series of principles that can guide the actions of those designing, implementing, and administering programs aimed at promoting the acquisition of knowledge about professionalism and the behaviors characteristic of a professional. It is the goal of this chapter to outline these principles. Any set of principles must be compatible with the complex nature of the medical curriculum through which individuals become transformed from members of the lay public into skilled professionals. There has not always been unanimity of opinion on how best to organize the teaching of professionalism. In part, this relates to individual and institutional approaches to the issue, with two schools of thought being predominant.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".