Amending Miller’s Pyramid to Include Professional Identity Formation
Bibliographic record
Abstract
In 1990, George Miller published an article entitled "The Assessment of Clinical Skills/Competence/Performance" that had an immediate and lasting impact on medical education. In his classic article, he stated that no single method of assessment could encompass the intricacies and complexities of medical practice. To provide a structured approach to the assessment of medical competence, he proposed a pyramidal structure with four levels, each of which required specific methods of assessment. As is well known, the layers are "Knows," "Knows How," "Shows How," and "Does." Miller's pyramid has guided assessment since its introduction; it has also been used to assist in the assessment of professionalism.The recent emphasis on professional identity formation has raised questions about the appropriateness of "Does" as the highest level of aspiration. It is believed that a more reliable indicator of professional behavior is the incorporation of the values and attitudes of the professional into the identity of the aspiring physician. It is therefore proposed that a fifth level be added at the apex of the pyramid. This level, reflecting the presence of a professional identity, should be "Is," and methods of assessing progress toward a professional identity and the nature of the identity in formation should be guided by currently available methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".