Situating requests for medical aid in dying within the broader context of end-of-life care: ethical considerations
Bibliographic record
Abstract
BACKGROUND: Medical aid in dying (MAiD) was introduced in Quebec in 2015. Quebec clinical guidelines recommend that MAiD be approached as a last resort when other care options are insufficient; however, the law sets no such requirement. To date, little is known about when and how requests for MAiD are situated in the broader context of decision-making in end-of-life care; the timing of MAiD raises potential ethical issues. METHODS: A retrospective chart review of all MAiD requests between December 2015 and June 2017 at two Quebec hospitals and one long-term care centre was conducted to explore the relationship between routine end-of-life care practices and the timing of MAiD requests. RESULTS: Of 80 patients requesting MAiD, 54% (43) received the intervention. The median number of days between the request for MAiD and the patient's death was 6 days. The majority of palliative care consults (32%) came less than 7 days prior to the MAiD request and in another 25% of cases occurred the day of or after MAiD was requested. 35% of patients had no level of intervention form, or it was documented as 1 or 2 (prolongation of life remains a priority) at the time of the MAiD request and 19% were receiving life-prolonging interventions. INTERPRETATION: We highlight ethical considerations relating to the timing of MAiD requests within the broader context of end-of-life care. Whether or not MAiD is conceptualised as morally distinct from other end-of-life options is likely to influence clinicians' approach to requests for MAiD as well as the ethical importance of our findings. We suggest that in the wake of the 2015 legislation, requests for MAiD have not always appeared to come after an exploration of other options as professional practice guidelines recommend.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".