The Journey towards Community-Based Dementia Care: The Destination, Roadmap, Guide, Tour Group and The Conditions
Bibliographic record
Abstract
In their paper, Morton-Chang et al. (2016) discuss how aging societies are struggling and trying to cope with the rapidly increasing numbers of persons living with dementia (PLWD). In that sense, the Canadian case is not unique. On the contrary, it is very similar to other developing countries. Therefore, it is worthwhile to reflect from another country's perspective on this unprecedented societal development. In this paper, I will consider the challenge of dealing with increasing numbers of PLWD from the European and, in particular, Dutch perspectives. Whereas, Morton-Chang et al. pose the question: "How do we get there from here?" I will address the issues of what the "there" should be and how we get there from my European/Dutch perspective. I will provide my view on how the roadmap can be drawn, who needs to be on the tour, who might be the guide and what conditions need to be in place to arrive at the desired destination.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 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".