Measuring and monitoring the quality of dying in the UN Decade of Healthy Ageing
Bibliographic record
Abstract
WHO aims to identify metrics to monitor the quality of dying, complementing those indicators proposed under the UN Decade of Healthy Ageing. However, the proposed criteria for a good death are contentious. Needs and priorities vary between individuals and their carers, across conditions, over time, and across communities and cultures. Monitoring should also consider sudden or rapid deaths and assisted dying. Fundamental challenges in data collection include who reports, over what timeframe, and when. This Personal View explores these challenges, identifying potentially measurable indicators and ambiguities in their use, and offers recommendations towards a practical measurement framework. We aimed to define a concise, meaningful, and pragmatic set of indicators that could be collected and applied universally across countries and over time. We define a logic model of candidate variables at different conceptual levels and describe an empirical exercise for prioritising and operationalising these variables for measurement.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".