Endgame: Philosophical, Clinical and Legal Distinctions between Palliative Care and Termination of Life
Bibliographic record
Abstract
Medical Assistance in Dying, Medical Aid in Dying, Physician-Assisted Death, Physician-Assisted Dying, Physician-Assisted Suicide all refer to the same thing: the intentional administration or provision of a lethal substance to a patient at the patient’s request to cause the patient’s death – in other words, the practice of termination of life by health care professionals. Recent Canadian dialogue surrounding legislative reform to permit physicians or other health care providers to provide termination of life frequently links termination of life practice to the practice of palliative care. For example, palliative care and termination of life have been described as activities falling on an end-of-life “spectrum” or “continuum of care”. The usage of such expressions, as well as aspects of the advocacy scoping and advancing termination of life practice in Canada, are contributing to a lack of clarity with respect to the substance of these two activities. The purpose of this discussion is to explore philosophical, clinical and legal distinctions between the practices of palliative care and termination of life in order to gain a better understanding of their substance, significance and relevance to end of life care, and in turn, ascertain reasons as to why it is critical to retain and promote such distinctions not only within the health care regulatory environment but also for the benefit of the public interest.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".