Bibliographic record
Abstract
BACKGROUND: Quality end-of-life care has emerged as an important concept in industrialized countries. DISCUSSION: We argue quality end-of-life care should be seen as a global public health and health systems problem. It is a global problem because 85 % of the 56 million deaths worldwide that occur annually are in developing countries. It is a public health problem because of the number of people it affects, directly and indirectly, in terms of the well being of loved ones, and the large-scale, population based nature of some possible interventions. It is a health systems problem because one of its main features is the need for better information on quality end-of-life care. We examine the context of end-of-life care, including the epidemiology of death and cross-cultural considerations. Although there are examples of success, we could not identify systematic data on capacity for delivering quality end-of-life care in developing countries. We also address a possible objection to improving end-of-life care in developing countries; many deaths are preventable and reduction of avoidable deaths should be the focus of attention. CONCLUSIONS: We make three recommendations: (1) reinforce the recasting of quality end-of-life care as a global public health and health systems problem; (2) strengthen capacity to deliver quality end-of-life care; and (3) develop improved strategies to acquire information about the quality of end-of-life care.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".