The Journey towards Community-Based Dementia Care: The Destination, Roadmap, Guide, Tour Group and The Conditions
Bibliographic record
Abstract
The Destination Before I discuss the destination -the "there," in terms of Morton-Chang et al. (2016) -I will first briefly depict my "here," The Netherlands.Geographically, The Netherlands is a small country, with a population of 17 million, of whom ~260,000 people suffer from dementia (RIVM 2016).The Dutch spend ~5.3% of their health budget on dementia (RIVM 2014).As in other countries, there are some signs that the prevalence is decreasing, probably because of improved prevention of vascular disease and higher levels of education (Larson et al. 2013;Matthews et al. 2013).Because of the sheer aging of the population, however, predictions are that in 2050, the number of PLWD will be ~500,000 in The Netherlands (Alzheimer Nederland 2013).Or, to give an impression of how it will affect society, in every street, there will be, on average, two PLWD.No doubt, dementia will affect the Dutch society to a large extent, as it will affect the Canadian society.Acknowledging that there will be no cure for dementia in the short term, society has to deal with this reality.It needs to take up the hazardous journey into developing communities that can accommodate PLWD.One argument for this journey is guided by normative principles: it is a human right that PLWD find a place in society and can participate without any discrimination, irrespective of disease or disability, as stated in the UN Convention on the Rights of Persons with Disabilities (UN 2006).Communities, therefore, should be accessible to all citizens, irrespective of the kinds of disabilities or impairments.Therefore, according to these principles, we need to develop dementia-friendly communities, that will be beneficial to other groups of people with disabilities as well.The second argument is an economic one.Calculated over a person's life, dementia isafter learning disabilities -the second most
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".