Expanded definitions of the ‘good death’? Race, ethnicity and medical aid in dying
Bibliographic record
Abstract
The range of end-of-life options is expanding across North America. Specifically, medical aid in dying (AID), or the process by which a patient with a terminal illness may request medical assistance with hastening death, has recently become legal in eight jurisdictions in the United States and all of Canada. Debates about AID often rely on cultural constructions that define some deaths as 'good' and others as 'bad'. While research has found commonalities in how patients, family members and health care providers define good and bad deaths, these constructions likely vary across social groups. Because of this, the extent to which AID is seen as a route to the good death also likely varies across social groups. In this article, we analyse qualitative data from six focus groups (n = 39) across three racial and ethnic groups: African American, Latino and white Californians, just after a medical AID law was passed. We find that definitions of the 'good death' are nuanced within and between groups, suggesting that different groups evaluate medical AID in part through complex ideas about dying. These findings further conversations about racial and ethnic differences in choices about end-of-life options.
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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 | 0.010 | 0.016 |
| 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 | 0.020 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".