A systematic review of older adults’ request for or attitude toward euthanasia or assisted-suicide
Bibliographic record
Abstract
OBJECTIVES: Prevalence rates of death by euthanasia (EUT) and physician-assisted suicide (PAS) have increased among older adults, and public debates on these practices are still taking place. In this context, it seemed important to conduct a systematic review of the predictors (demographic, physical health, psychological, social, quality of life, religious, or existential) associated with attitudes toward, wishes and requests for, as well as death by EUT/PAS among individuals aged 60 years and over. METHOD: = 306) were excluded. RESULTS: This review identified 21 studies with predictive analyses, but in only 4 did older adults face actual end-of-life decisions. Most studies (17) investigated attitudes toward EUT/PAS (9 through hypothetical scenarios). Younger age, lower religiosity, higher education, and higher socio-economic status were the most consistent predictors of endorsement of EUT/PAS. Findings were heterogeneous with regard to physical health, psychological, and social factors. Findings were difficult to compare across studies because of the variety of sample characteristics and outcomes measures. CONCLUSION: Future studies should adopt common and explicit definitions of EUT/PAS, as well as research designs (e.g. mixed longitudinal) that allow for better consideration of personal, social, and cultural factors, and their interplay, on EUT/PAS decisions.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".