[Are academic radiologists evaluating the professional ethics of their residents?].
Bibliographic record
Abstract
PURPOSE: Ethics is a challenging topic to teach and calls for competencies complex to pin point. Nevertheless, evaluation of ethics is a milestone on the way to autonomous medical practice. Our objectives were 1; To describe the "hidden knowledge" of academic radiologists evaluating the ethics of their residents; 2; To look for common denominators by comparing the evaluative practice of academic radiologists from France and Quebec. METHOD: 8 academic radiologists were interviewed on a critical episode of ethics evaluation of residents. The 8 transcripts were coded then co-coded using a USA reference from the radiology literature. RESULTS: 8/8 radiologists referred to 8 common issues of ethics, 7/8 shared an extra one, related to the teaching setting and 3/8 added the specific issue of consent to radiological procedure. CONCLUSION: The same ethical issues are shared by radiologists working in USA, France and Quebec. There are great similarities in evaluating ethics of residents among academic radiologists from France and Quebec, the diffusion of which being potentially useful to medical ethics training.
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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 | 0.008 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".