Place of death in the population dying from diseases indicative of palliative care need: a cross-national population-level study in 14 countries
Bibliographic record
Abstract
BACKGROUND: Studying where people die across countries can serve as an evidence base for health policy on end-of-life care. This study describes the place of death of people who died from diseases indicative of palliative care need in 14 countries, the association of place of death with cause of death, sociodemographic and healthcare availability characteristics in each country and the extent to which these characteristics explain country differences in the place of death. METHODS: Death certificate data for all deaths in 2008 (age ≥1 year) in Belgium, Canada, the Czech Republic, England, France, Hungary, Italy, Mexico, the Netherlands, New Zealand, South Korea, Spain (Andalusia), the USA and Wales caused by cancer, heart/renal/liver failure, chronic obstructive pulmonary disease, diseases of the nervous system or HIV/AIDS were linked with national or regional healthcare statistics (N=2,220,997). RESULTS: 13% (Canada) to 53% (Mexico) of people died at home and 25% (the Netherlands) to 85% (South Korea) died in hospital. The strength and direction of associations between home death and cause of death, sociodemographic and healthcare availability factors differed between countries. Differences between countries in home versus hospital death were only partly explained by differences in these factors. CONCLUSIONS: The large differences between countries in and beyond Europe in the place of death of people in potential need of palliative care are not entirely attributable to sociodemographic characteristics, cause of death or availability of healthcare resources, which suggests that countries' palliative and end-of-life care policies may influence where people die.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".