Supporting the development of a professional identity: General principles
Bibliographic record
Abstract
While teaching medical professionalism has been an important aspect of medical education over the past two decades, the recent emergence of professional identity formation as an important concept has led to a reexamination of how best to ensure that medical graduates come to "think, act, and feel like a physician." If the recommendation that professional identity formation as an educational objective becomes a reality, curricular change to support this objective is required and the principles that guided programs designed to teach professionalism must be reexamined. It is proposed that the social learning theory communities of practice serve as the theoretical basis of the curricular revision as the theory is strongly linked to identity formation. Curricular changes that support professional identity formation include: the necessity to establish identity formation as an educational objective, include a cognitive base on the subject in the formal curriculum, to engage students in the development of their own identities, provide a welcoming community that facilitates their entry, and offer faculty development to ensure that all understand the educational objective and the means chosen to achieve it. Finally, there is a need to assist students as they chart progress towards becoming a professional.
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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".