Role Modeling in Physicians??? Professional Formation: Reconsidering an Essential but Untapped Educational Strategy
Bibliographic record
Abstract
Forming technically proficient, professional, and humanistic physicians for the 21st century is no easy task. Mountains of biomedical knowledge must be acquired, diagnostic competence achieved, effective communication skills developed, and a solid and applicable understanding of the practice and role of physicians in society today must be reached. The central experience for learners in this complex and challenging terrain is the "modeling of" and "learning how to be" a caregiver and health professional. Role modeling remains one crucial area where standards are elusive and where repeated negative learning experiences may adversely impact the development of professionalism in medical students and residents. The literature is mainly descriptive, defining the attributes of good role models from both learners and practitioners' perspectives. Because physicians are not "playing a role" as an actor might, but "embodying" different types of roles, the cognitive and behavioral processes associated with successfully internalizing roles (e.g., the good doctor/medical educator) are important. In this article, the authors identify foundational questions regarding role models and professional character formation; describe major social and historical reasons for inattention to character formation in new physicians; draw insights about this important area from ethics and education theory (philosophical inquiry, apprenticeship, situated learning, observational learning, reflective practice); and suggest the practical consequences of this work for faculty recruitment, affirmation, and development.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".