Bibliographic record
Abstract
As physicians, patients, and members of the general public have come to believe that medicine's professionalism is under threat, virtually all have concluded that any action to address the issue must include a major educational initiative aimed at ensuring that physicians both understand the nature of contemporary medical professionalism and live according to its precepts. As a result, there is now a substantial literature containing a variety of opinions as to how this can be best accomplished. One of the common themes that has emerged is that the approaches of the past are no longer sufficient. For centuries, professionalism as a subject was not addressed directly. There were no courses on professionalism and it was not included in the standard medical curriculum. This is not because it was deemed unimportant. The Hippocratic Oath, subsequent codes of ethics, and a host of writers including Osler addressed the values and beliefs of the medical profession, often linking them to the word professionalism. However, it was assumed that these values and beliefs, which are the foundation of the profession, would be acquired during the process of socialization of students as they “acquire the complex ensemble of analytic thinking, skillful practice, and wise judgment.” The learning of professionalism depended heavily upon role models where students, residents, and indeed practicing physicians patterned their behavior on “individuals admired for their ways of being and acting as professionals.” While this method remains essential and powerful, by itself it is no longer felt to be adequate.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".