Quality end-of-life care: A global perspective
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.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".