Addressing ethical issues in geriatrics and long-term care: ethics education at the Baycrest Centre for Geriatric Care.
Bibliographic record
Abstract
An innovative program in ethics education exists at Baycrest Centre for Geriatric Care. This program can serve as a helpful model for long-term care and geriatric care facilities seeking to implement formal training programs in bioethics. Various aspects of the ethics education program are examined. In addition to describing the role of the ethics committee and research ethics board, consideration is given to case consultations, ethics rounds, the training of junior physicians and medical students, grand rounds and the planning of conferences and guest lectures. With regard to educational content in bioethics, health law, professional guidelines and the principlist approach of Beauchamp and Childress are used to explore the ethical dimensions of particular cases. Given the clinical context of the educational initiatives, the pedagogical approach is predominately case-based. While the bioethics literature emphasizes the patient-physician relationship, ethics education at Baycrest recognizes the importance of multiple professions. Physicians, nurses, social workers, speech pathologists, nutritionists and other health care providers are involved in ethical deliberation and 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".