Medical Assistance in Dying: Alberta Approach and Policy Analysis
Bibliographic record
Abstract
ABSTRACTThe legalization of medical assistance in dying (MAID) in Canada has presented an opportunity for physicians, policy makers, and patients to rethink end-of-life care. This article reviews the key features of the Alberta MAID framework and puts it in the context of other provinces and their MAID programs. We also compared policies and MAID practices in different provinces/territories of Canada. In addition, we used the Alberta MAID database to provide the current state of patient demographics and access to MAID services in Alberta in 2017-2018. Significant differences were identified between provincial/territorial MAID program processes and practices. Alberta, Ontario, and Quebec have more comprehensive frameworks. Alberta has dedicated resources to the support of MAID. The median age of those who received MAID service in Alberta from July 2017 to April 2018 was 70 years; a higher proportion were males (55%) and the majority included patients with a cancer diagnosis (70%). Approximately 39 per cent of MAID events happened in a hospital setting, and 38 per cent occurred in patients' homes. We have presented some recommendations on MAID program development, implementation, and review based on Alberta's experience with MAID over the past two years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".