Modern Conceptions of Elite Medical Practice Among Internal Medicine Faculty Members
Bibliographic record
Abstract
BACKGROUND: To understand the modern conceptions of elite practice informing the hidden curriculum through use of peer nominations asking clinicians to identify exceptional practitioners. METHOD: We distributed a Web-based survey to Department of Medicine faculty at five universities in North America. Participants were asked to nominate individuals they deemed to be "outstanding practitioners" and to provide reasons. They were then asked to nominate "exceptional diagnosticians" and "exceptional professionals." RESULTS: Two hundred eighty-two physicians nominated 558 unique peers as "outstanding practitioners." Justifications included knowledge (45.1%), patient-related interpersonal skill (18.7%), teaching skill (10.8%), and research success (6.8%). More "exceptional diagnostician" nominees were nominated as "outstanding practitioners" (65.2%) relative to "exceptional professional" nominees (56.1%), although the effect size was small (phi = 0.09). CONCLUSIONS: Knowledge-based competencies maintain a central role in modern conceptions of elite medical practice, although, contrary to the historical dominance of biomedical abilities, a diverse set of skills and professional aptitudes are also well represented.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".