National Dementia Strategies: What Should Canada Learn?
Bibliographic record
Abstract
BACKGROUND: In order to provide appropriate care for the aging population, many countries are adopting a National Dementia Strategy (NDS). On June 22, 2017, Canada announced it will become the 30th country to launch a NDS. In light of this announcement and as Canada prepares to develop its own NDS, we conducted this review to examine and compare the NDSs of the other previous 29 countries with Canadian government's policies to date. METHODS: NDSs were compared according to their major priorities. The primary endpoints were the framework conditions and key actions outlined in the strategies. Secondary endpoints included the years active, involvement of stakeholders, funding, and implementation. RESULTS: We were able to review and compare 25 of the 29 published NDSs. While the NDSs of each country varied, several major priorities were common among the strategies-increasing awareness of dementia, reducing its stigma, identifying support services, improving the quality of care, as well as improving training and education and promoting research. CONCLUSIONS: This review comprehensively lists and compares the NDSs of different countries. The results should be of great interest to policy-makers, health-care professionals and other key stakeholders involved in developing Canada's forthcoming NDS. We hope that policy-makers in Canada can review other NDSs, learn from their example, and develop an effective NDS for our country.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".