The Teaching of Professionalism: Vignettes for Discussion
Bibliographic record
Abstract
FOR UNDERGRADUATE PRE-CLINICAL MEDICAL STUDENTS First identify the elements, characteristics, or attributes of professionalism raised by each of the following cases. You may then discuss solutions to the problem. Case #1 You notice a colleague reading the chart of a patient who is a personal friend of the colleague. He has not been involved in the patient's care. You know that the chart has some sensitive personal information in it. Case #2 The medical school assigns families to first-year students for early clinical experience. You are on an elevator and hear a student discussing his assigned family, including the names of the family, in derogatory terms. Case #3 A drug company representative gives you a stethoscope with the name of an expensive cardiac medicine prominently displayed on it. Case #4 A student observes an exemplary student during the end of semester final using a textbook during a major closed book examination. Case #5 A patient and family that you have been assigned to follow gives you information that may influence care and asks that you do not tell anyone, including the patient's doctor. FOR UNDERGRADUATE CLINICAL MEDICAL STUDENTS First identify the elements, characteristics, or attributes of professionalism raised by each of the following cases. You may then discuss solutions to the problem. Case #1 A final-year medical student believes the attending surgeon is inebriated while performing an operation. Case #2 A senior resident asks a medical student to put in an arterial line. The student has never seen or performed this procedure before. The resident explains the technique, then tells the student to proceed and leaves.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".