Clinical education of ethicists: the role of a clinical ethics fellowship
Bibliographic record
Abstract
BACKGROUND: Although clinical ethicists are becoming more prevalent in healthcare settings, their required training and education have not been clearly delineated. Most agree that training and education are important, but their nature and delivery remain topics of debate. One option is through completion of a clinical ethics fellowship. METHOD: In this paper, the first four fellows to complete a newly developed fellowship program discuss their experiences. They describe the goals, structure, participants and activities of the fellowship. They identify key elements for succeeding as a clinical ethicist and sustaining a clinical ethics program. They critically reflect upon the challenges faced in the program. RESULTS: The one-year fellowship provided real-time clinical opportunities that helped them to develop the necessary knowledge and skills, gain insight into the role and scope of practice of clinical ethicists and hone valuable character traits. CONCLUSION: The fellowship enabled each of the fellows to assume confidently and competently a position as a clinical ethicist upon completion.
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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 | 0.288 | 0.911 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.100 |
| Insufficient payload (model declined to judge) | 0.001 | 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".