Medicalisation, suffering and control at the end of life: The interplay of deep continuous palliative sedation and assisted dying
Bibliographic record
Abstract
Medicalisation is a pervasive feature of contemporary end of life and dying in Western Europe and North America. In this article, we focus on the relationship between two specific aspects of the medicalisation of dying: deep continuous palliative sedation until death and assisted dying. We draw upon a qualitative interview study with 29 health professionals from three jurisdictions where assisted dying is lawful: Flanders, Belgium; Oregon, USA; and Quebec, Canada. Our findings demonstrate that the relationship between palliative sedation and assisted dying is often perceived as fluid and complex. This is inconsistent with current laws as well as with ethical and clinical guidelines according to which the two are categorically distinct. The article contributes to the literature examining health professionals' opinions and experiences. Moreover, our findings inform a discussion about emergent themes: suffering, timing, autonomy and control - which appear central in the wider discourse in which both palliative sedation and assisted dying are situated, and which in turn relate to the wider ideas about what constitutes a 'good death'.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".