Bibliographic record
Abstract
We thank Dr. Barnhoorn for having entered into a discussion of professional identity formation and Miller’s pyramid. We are pleased to see that he is concerned about issues of professional behaviors and professionalism. However, we take exception with some elements of his letter. First, we did not suggest that professional identity formation replace the concepts of professional behavior and professionalism. In earlier publications, we indicated our belief that making the acquisition of a professional identity an educational objective builds upon experience gained from teaching professionalism.1,2 Furthermore, in acquiring a professional identity, the norms of behavior expected of a future physician are those traditionally associated with professionalism. The explicit teaching of professionalism will thus remain necessary. Second, we remind Dr. Barnhoorn that our purpose in amending Miller’s pyramid was not to clarify the nature of professional identity formation but, rather, to broaden the scope of assessment in this emerging field. Miller’s pyramid has become a widely used framework within which the multiple levels of mastery over the art and science of medicine can be assessed.3 It was not meant to assist in the understanding of the educational process, nor have we used it for that purpose. If the development of a professional identity is to be an educational objective, some method of determining whether individuals have achieved this objective becomes necessary. While this may be “idealistic,” it has been shown to be feasible in medicine and other professions.3 As Miller’s pyramid has been used extensively to develop programs for the assessment of professionalism and professional behaviors, our intention is to expand upon this framework to accommodate the concept of professional identity formation. The “onion model” invoked by Dr. Barnhoorn may or may not be helpful in understanding professional identity formation. However, it does not appear to be useful in assessing progress towards the development of a professional identity. Richard L. Cruess, MD Professor of surgery and core faculty member, Centre for Medical Education, McGill University, Montréal, Québec, Canada; [email protected] Sylvia R. Cruess, MD Professor of medicine and core faculty member, Centre for Medical Education, McGill University, Montréal, Québec, Canada. Yvonne Steinert, PhD Professor of family medicine and director, Centre for Medical Education, McGill University, Montréal, Québec, Canada.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.025 | 0.055 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".