What would it take to die well? A systematic review of systematic reviews on the conditions for a good death
Bibliographic record
Abstract
The medicalisation of life under the influence of health-care systems, focused on curing diseases, has made dying well challenging. This systematic review identifies common themes from published systematic reviews about the conditions for a good death as a means to guide decisions around this universal event. MEDLINE, Embase, APA PsycInfo, and AMED were searched for citations with "good death" or "dying well" in their titles on Sept 23, 2020, and complemented with backward reference and forward citation screening with Google Scholar. Articles published in peer-reviewed journals in any language were included. Articles that focused on the identification of conditions for a good death and described how primary studies were sought and selected were also included. Data on general characteristics, quality, and themes were extracted independently. 13 of 275 potentially eligible reviews were included. Common themes were dying at the preferred place, relief from pain and psychological distress, emotional support from loved ones, autonomous treatment decision making, avoidance of futile life-prolonging interventions and of being a burden to others, right to assisted suicide or euthanasia, effective communication with professionals, and performance of rituals. No reviews specified the meaning or timing of death, connected themes, or prioritised them. Vague jargon was often used to describe complex concepts. Most conditions for a good death could be offered to most dying people, without costly medical infrastructure or specialised knowledge. Efforts to describe these conditions clearly, to identify whether there are exceptions or missing items, and whether they apply in non-dominant settings (ie, outstide institutional, affluent, anglophone, and Christian settings) are needed.
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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 | 0.059 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".