NATIONAL ALZHEIMER’S PLANS AND INITIATIVES: LESSONS LEARNED FROM IMPLEMENTATION
Bibliographic record
Abstract
Alzheimer disease and related disorders have been recognized as a public health priority by WHO. Almost 20% of Baby-Boomers will suffer from Alzheimer’s disease (AD) and related disorders during their lifetime. With the increasing number of older persons, Alzheimer’s disease continues to be a major global public health issue. This will be even more imperative with the advent in the coming years of biomarkers and disease modifying medications. Central to all these efforts is the objective to provide access to personalized, co-ordinated assessment and treatment services for people with AD and their caregivers. Many jurisdictions across the world have developed initiatives to improve care of persons with dementia while some have actually adopted and implemented Alzheimer plans, with major efforts to develop and implement innovative collaborative care policies and models to improve early detection, access and care for patients and their caregivers. The French plan is focused on specialty care while plans in Canada and Israel as well as initiatives in the USA are anchored in primary medical care. The objectives of this symposium are to: 1) discuss the key characteristics of initiatives and policies implemented in Canada, USA, France and Israel; 2) present data on the impact of these initiatives, models of care and policies on the detection, diagnosis, treatment of dementia, continuity of care, quality of follow-up, coordination between primary care and specialty care, satisfaction; 3) examine policies implementation strategies and identify key factors for successful development and large scale implementation.
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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 | 0.094 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".