Hastening Death in Canadian ICUs: End-of-Life Care in the Era of Medical Assistance in Dying*
Bibliographic record
Abstract
OBJECTIVES: Since 2016, Canada has allowed for euthanasia based on strict criteria under federal medical assistance in dying legislation. The purpose of this study was to determine how Canadian intensivists perceive medical assistance in dying and whether they believe their approach to withdrawal of life-sustaining therapies has changed following introduction of medical assistance in dying. DESIGN: Electronic survey. SETTING: Participants were recruited from 11 PICU programs and 14 adult ICU programs across Canada. All program leaders for whom contact information was available were approached for participation. PARTICIPANTS: We invited intensivists and critical care trainees employed between December 2019 and May 2020 to participate using a snowball sampling technique in which department leaders distributed study information. All responses were anonymous. Quantitative data were analyzed using descriptive statistics. Categorical variables were analyzed using Pearson chi-square test. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: We obtained 150 complete questionnaires (33% response rate), of which 50% were adult practitioners and 50% pediatric. Most were from academic centers (81%, n = 121). Of respondents, 86% (n = 130) were familiar with medical assistance in dying legislation, 71% in favor, 14% conflicted, and 11% opposed. Only 5% (n = 8) thought it had influenced their approach to withdrawal of life-sustaining therapies. Half of participants had no standardized protocol for withdrawal of life-sustaining therapies in their unit, and 41% (n = 62) had observed medications given in disproportionately high doses during withdrawal of life-sustaining therapies, with 13% having personally administered such doses. Most (80%, n = 120) had experienced explicit requests from families to hasten death, and almost half (47%, n = 70) believed it was ethically permissible to intentionally hasten death following withdrawal of life-sustaining therapies. CONCLUSIONS: Most Canadian intensivists surveyed do not think that medical assistance in dying has changed their approach to end of life in the ICU. A significant minority are ethically conflicted about the current approach to assisted dying/euthanasia in Canada. Almost half believe it is ethical to intentionally hasten death during withdrawal of life-sustaining therapies if death is expected.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".