Medical Assistance in Dying: An Ethnographic Study on the Practitioner’s Decision Making in Eligibility Assessments
Bibliographic record
Abstract
In a historic ruling on the 6th of February 2015, the Supreme Court of Canada declared that sections of the Criminal Code of Canada prohibiting medical assisted dying were no longer valid. Following the court's mandate, government laws and provincial policies were passed to facilitate the implementation of this ruling. Regulatory bodies implemented frameworks and policies on the healthcare practitioner's practice of care in assisted dying. This ethnography aimed to examine how Albertan medical assistance in dying (MAiD) assessors and providers understand and apply Alberta Health Services (AHS) policies in determining a patient's eligibility for MAiD provisions. Eight healthcare practitioners participated in semi-structured, in-depth interviews engaging their understanding of critical components of the policies and legislation on MAiD. The three main themes include 1) communication with the patient, 2) the practitioner's comfort level, and 3) the patient's life context. Practitioners centred their decision-making on communication, as well as the relationship between the patient and family. This demonstrates that policies need to reflect the important role of family members in end-of-life care and the practitioner's MAiD eligibility decision-making.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".