The Moral Dissociation Curve, Blind Spots and Prescribing Death in Canada
Bibliographic record
Abstract
Provider assisted death is becoming a leading cause of death in Canada since the passage of Medical Assistance in Dying (MAiD) legislation in 2016. What was to be exceptional has now become common; some are calling for it to be expected. Increasing numbers of patients with chronic, non-terminal conditions are being euthanized. Healthcare personnel are now approving and offering MAiD to vulnerable patients who are depressed, disabled, chronically ill or impoverished. This paper presents a rationale from a transcendent moral law perspective, traditionally called natural law, for why Canada now has the most liberal euthanasia regime in the world. The act of euthanasia requires the provider to willfully end the life of the patient by administering a lethal substance. This violates the transcendent moral law, do not kill. Once a culture willfully rejects this fundamental law and embraces a utilitarian ethic devoid of any principle except the notion of autonomy, it is inevitable that the practice will lead to ethical ambiguity and uncertainty. As the practice persists and becomes the norm, moral blindness develops which leads to gross abuses to human beings. I present an ethical diagram, the Moral Dissociation Curve, that depicts the reason for the trends unfolding in Canada. The Canadian healthcare system must re-affirm the principles of the Hippocratic Ethic and the inherent dignity of their patients. Those in healthcare need to prioritize high quality, compassionate, palliative care and say “no” to willfully ending the lives of suffering patients. In so doing, moral clarity will be re-gained, and society’s most vulnerable will be protected.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".