Estimating the need for palliative care at the population level: A cross-national study in 12 countries
Bibliographic record
Abstract
BACKGROUND: To implement the appropriate services and develop adequate interventions, detailed estimates of the needs for palliative care in the population are needed. AIM: To estimate the proportion of decedents potentially in need of palliative care across 12 European and non-European countries. DESIGN: This is a cross-sectional study using death certificate data. SETTING/PARTICIPANTS: All adults (⩾18 years) who died in 2008 in Belgium, Czech Republic, France, Hungary, Italy, Spain (Andalusia, 2010), Sweden, Canada, the United States (2007), Korea, Mexico, and New Zealand ( N = 4,908,114). Underlying causes of death were used to apply three estimation methods developed by Rosenwax et al., the French National Observatory on End-of-Life Care, and Murtagh et al., respectively. RESULTS: The proportion of individuals who died from diseases that indicate palliative care needs at the end of life ranged from 38% to 74%. We found important cross-country variation: the population potentially in need of palliative care was lower in Mexico (24%-58%) than in the United States (41%-76%) and varied from 31%-83% in Hungary to 42%-79% in Spain. Irrespective of the estimation methods, female sex and higher age were independently associated with the likelihood of being in need of palliative care near the end of life. Home and nursing home were the two places of deaths with the highest prevalence of palliative care needs. CONCLUSION: These estimations of the size of the population potentially in need of palliative care provide robust indications of the challenge countries are facing if they want to seriously address palliative care needs at the population level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".