Teaching bioethics to medical students and postgraduate trainees in the clinical setting
Bibliographic record
Abstract
As he reviews the curriculum for his surgical residency training program, Dr. A is concerned about how to prepare his residents to gain understanding of biomedical ethics as it relates to the specialty and to use their understanding to improve patient care (Royal College of Physicians and Surgeons of Canada, 2001). Last year, he invited a moral philosopher to give a guest lecture, which focused on theoretical issues with no reference to how these concepts relate to clinical experience. The residents' evaluations were unfavorable: “a waste of our time,” “not relevant to the problems we face.” Recently, the residents and nurses were troubled by a difficult situation on the ward: Mr. B, a 46-year-old patient, was found to have unresectable pancreatic cancer, but his wife insisted that the staff withhold the diagnosis from him because he is prone to depression. Dr. A wonders whether this situation could serve as a learning opportunity for the residents and staff and whether he should try to lead a seminar about this problem. He pages the chief resident. What is bioethics teaching and why is it important? Bioethics is now taught in most medical schools as part of the standard curriculum. Many accrediting bodies require residency training programs to teach bioethics as a condition of approval, and there is increasing interest in bioethics in continuing medical education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".