Physicianship: Educating for Professionalism in the Post-Flexnarian Era
Bibliographic record
Abstract
Although he did not write extensively about professionalism, Abraham Flexner clearly understood its critical role in medical practice. In discerning the basics of medical education he characterized scientific methodology as the instrumental minimum. He left open to future generations the task of defining its necessary complement, the "noble behaviors and fine feelings" required of the medical practitioner. Situated within the current professionalism movement, and informed by previous commentary on the enduring attributes of medicine, a curriculum based on "Physicianship"--the physician as healer and professional--can serve as a logical post-Flexnerian curriculum. The conceptual armature of Physicianship and the attributes necessary for the fulfillment of both the professional and healer role can assist in the selection of students and constitute the educational blueprint for medical teaching. The critically important concepts of identity formation and the requirements for the valid and reliable assessment of professional behaviors of students and faculty are essential components. A Physicianship curriculum, as conceived and deployed at the McGill University Faculty of Medicine, might resonate with Flexner.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".