Bibliographic record
Abstract
There are educational principles that apply to the teaching of professionalism during undergraduate education and postgraduate training. It is axiomatic that there is a single cognitive base that applies with increasing moral force as students enter medical school, progress to residency or registrar training, and enter practice. While parts of this body of knowledge are easier to teach and learn at different stages of an individual's career, it remains a definable whole at all times and should be taught as such. While the principle that self-reflection on theoretical and real issues encountered in the life of a student, resident or practitioner is essential to the acquisition of experiential learning and the incorporation of the values and behaviors of the professional, the opportunities to provide situations where this can take place will change as an individual progresses through the system, as will the sophistication of the level of learning. Teaching the cognitive base of professionalism and providing opportunities for the internalization of its values and behaviors are the cornerstones of the organization of the teaching of professionalism at all levels. Situated learning theory appears to provide practical guidance as to how this may be implemented. While the application of this theory will vary with the type of curriculum, the institutional culture and the resources available, the principles outlined should remain constant.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".