[no title]
Bibliographic record
Abstract
The World Alzheimer Report 2016, Improving healthcare for people living with dementia: Coverage, quality and costs now and in the future, reviews research evidence on the elements of healthcare for people with dementia, and, using economic modelling, suggests how it should be improved and made more efficient. The report argues that current dementia healthcare services are over-specialised, and that a rebalancing is required with a more prominent role for primary and community care. This would increase capacity, limit the increased costs associated with scaling up coverage of care, and, coupled with the introduction of care pathways and case management, improve the coordination and integration of care. Modelling of the costs of care pathways was carried out in Canada, China, Indonesia, Mexico, South Africa, South Korea and Switzerland, to estimate the costs of dementia healthcare under different assumptions regarding delivery systems. The report was researched and authored by Prof Martin Prince, Ms Adelina Comas-Herrera, Prof Martin Knapp, Dr Maëlenn Guerchet and Ms Maria Karagiannidou from The Global Observatory for Ageing and Dementia Care, King’s College London and the Personal Social Services Research Unit (PSSRU), London School of Economics and Political Science.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".