Professionalism in the Health Sciences: Lessons Learned from its Definition, Evaluation, and Teaching in a Medical School
Bibliographic record
Abstract
For the past 15 years, the medical profession has been concerned about the professionalism, or lack thereof, exhibited by many physicians, and similar concerns are evident for the broad scope of health professions. This article summarizes the efforts of one school of medicine to define the problem, embark on a program to improve the situation, and evaluate its progress. Because the author's experience is within the realm of human medicine and the education of medical students, the article has been written from this perspective, but it is hoped that readers in the other health professions will find this account of benefit. While the vignettes presented here are taken from human medicine, readers in other health professions should be readily able to translate them into their own spheres of interest.
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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 | 0.140 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".