“Current dementia care: what are the difficulties and how can we advance care globally?”
Bibliographic record
Abstract
BACKGROUND: Dementia is a growing global public health concern, with post-diagnostic care often very limited. Depending on where people live, both within a country and depending on high-, middle-, and low-income countries, they might also face barriers in accessing the right care at the right time. Therefore, it is important to highlight recent evidence on the facilitators and barriers to dementia care, but also evidence on how to move dementia care forward. MAIN TEXT: Current dementia care is subject to several inequalities, including living in rural regions and being from a minority ethnic background. Evidence in this collection highlights the issues that both people living with dementia and unpaid carers are facing in accessing the right care, with evidence from Australia, Canada, Uganda, to the Netherlands, and further afield. Providing improved dementia-specific training to health care professionals and supporting medication and reablement interventions have been identified as possible ways to improve dementia care for all. CONCLUSIONS: This special issue focuses on recent evidence on inequalities in dementia care across the globe and how dementia care can be advanced in various areas.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".