Bibliographic record
Abstract
Worldwide, 56 million people die each year, 85% of these in developing countries. 1 2 Yet little is known about the quality of care they receive at the end of their lives. The movement for improving the quality of care at the end of life is primarily focused on industrialised countries. Until it is seen as a global problem for public health and health systems, efforts to improve it will not make much impact in the world. Quality of care at the end of life is a global public health problem because of the large number of people involved. If each death affects five other people in terms of giving informal care and grieving relatives and friends, the total number of people affected each year by end of life care is about 300 million, or 5% of the world's population. Some of the interventions that could be used to improve care are in the realm of public health. These include large scale, culturally specific, educational programmes for public health workers and the public; population based strategies to destigmatise death and put it into the mainstream of health systems; and changes in social policies in relation to care for orphans. Improving care at the end of life will require research in public health. Of the many papers …
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".