Conclusion: Navigating 21st-Century Remote and Rural Dementia Care and a Future Research Agenda
Bibliographic record
Abstract
Even though the intention is to offer the whole population services of comparable quality regardless of where they live, there are some challenges with living in rural areas…. (Kirkevold and Kristiansen, from Chapter 4 in this book) Drawing across the material in this book, in this chapter we raise and discuss the emergent themes as highlighted by the contributions of leading experts and commentators from around the world. In terms of policy we consider systems issues and between-countries similarities and differences; for practice , we examine the relevance of culture and the importance of heeding the central human experience of dementia within the current healthcare system; and in respect of key emergent research topics, we feature the relevance of place-based planning and the role of technology. We end with a collated research agenda, drawing from topics suggested by authors across the chapters. Policy and systems Dementia is a costly issue for governments as people with dementia can live for a long time following their diagnosis, variably requiring different health and social care inputs (Prince et al, 2015). The World Health Organization (WHO) has taken a lead in establishing the need for coordinated national policy and planning approaches (2018), and has provided a toolkit for planning, education and community engagement (WHO, 2017), an online training programme for dementia carers (iSupport for Dementia) (WHO, 2020), and a knowledge exchange platform with access to key dementia data and indicators so that progress in meeting global dementia targets can be evaluated (Global Dementia Observatory) (WHO, 2019). The mhGAP toolkit can be adopted by individual countries and adapted to local systems and contexts. The chapters in this book highlight the relevance of countries’ health systems and the contexts in which services are provided, as well as the characteristics of such services. By one interpretation, chapters depict health systems with features across a wide spectrum, from those with atomised services provided by a mixture of public, private and nongovernmental organisations (in Australia), through those that have become depleted (in Ireland), to apparently more coordinated and adaptive social welfarist models (such as those in Austria and Norway).
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".