Teaching old dogs new tricks—a personal perspective on a decade of efforts by a clinical ethics committee to promote awareness of medical ethics
Bibliographic record
Abstract
To incorporate medical ethics into clinical practice, it must first be understood and valued by health care professionals. The recognition of this principle led to an expanding and continuing educational effort by the ethics committee of the Vancouver General Hospital. This paper reviews this venture, including some pitfalls and failures, as well as successes. Although we began with consultants, it quickly became apparent that education in medical ethics must reach all health care professionals--and medical students as well. Our greatest successes came in the formative years of a medical career (i.e., in medical school and residency training programmes), but other efforts were not wasted, particularly among nurses and other health care professionals. Although this is a personal review of the experience in one institution, the lessons learnt in Vancouver are applicable to the further development of medical ethics in the UK.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.341 | 0.811 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.247 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads agree on what is shown here.
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".