Bibliographic record
Abstract
BACKGROUND: Ethics training is required for all radiology residents in Canada, but this may be difficult to provide as radiology departments may not have radiologists with formal ethics training, and may not have access to educational resources focussed on teaching ethics to radiologists. We describe the implementation of a case-based approach to teaching and learning ethics, designed for Canadian radiologists. This approach can be adapted for use in other specialties through development of specialty-specific ethics case scenarios. METHODS: Ethics case study rounds specific to Canadian radiologic practice were presented at two different institutions, and using two different methods within one institution. In one method, we requested that the residents read the case study and questions ahead of time; in the other, the rounds were presented without any expectation of residents doing prior preparation. RESULTS: The participants, as a group, agreed with all seven survey statements describing the value of the experience. The opportunity to read the case ahead of time seemed helpful for some residents, but was not found to be overall more useful than discussing the case without prior review. Indeed, more than half of the resident participants in this group indicated that they did not make use of the advance materials at all. CONCLUSION: Resident feedback indicates that ethics case study rounds are a useful and valuable experience, especially when the case is specifically tailored to their medical practice. Prior preparation was not necessary for residents to benefit from these rounds.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".