Bibliographic record
Abstract
The greatest difficulty in life is to make knowledge effective, to convert it into practical wisdom. Sir William Osler The challenge of teaching and learning professionalism has been highlighted by many authors. The increasing complexity of the practice of medicine, coupled with the entry of the state and corporate sector into the health care field, has drastically altered the relationship between the medical profession and the society it serves. At the same time, role modeling, the traditional method for transmitting professional values from one generation to the next, is no longer sufficient. Professionalism must be taught explicitly. Despite consensus on the importance of teaching and learning professionalism, many clinical teachers are not able to articulate the attributes and behaviors characteristic of the physician as a professional. Many faculty members are also not sure of how to best teach and evaluate this content area and may not be serving as effective role models. As a result, faculty development is needed to ensure the successful teaching and learning of professionalism. To date, the literature on faculty development designed to support the teaching and evaluation of professionalism is limited. The goal of this chapter is to outline the principles and strategies underlying faculty development programming in this area and to provide a case example from our own institution. Faculty development refers to that broad range of activities institutions use to renew or assist faculty in their multiple roles.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".