O2‐04‐01: Impact of the g7 initiative on the global dementia policy agenda
Bibliographic record
Abstract
The number of people with Alzheimer's disease and other dementias is increasing worldwide and these diseases mean a growing burden to health and social care systems. The growing concerns within governments have led to the G7 initiative ‘Global Action Against Dementia’ that started with a Summit in London, UK, in December 2013. The summit concluded with a Declaration and a number of follow up events were planned, in London (UK) in June 2014, Ottawa (Canada) in September 2014, Tokyo (Japan) in November 2014, Washington DC (USA), February 2015 and finally at the World Health Organization, Geneva in March 2015. The UK government also appointed a World Dementia Envoy and World Dementia Council. A number of workstreams were agreed during this initiative and additional events and meetings were held on technology, regulation, contribution of charitable organizations as well as Young Leaders events. A number of developments have been agreed and (partly) implemented including optimizing the pathway for new medicines, additional funding for research in for instance USA, Canada, UK and Japan, a new fund for early stage research. Italy, Japan and France presented new national dementia plans and Canada announced to start the process towards their first plan. Non-profit organizations enhanced their collaboration on awareness and societal change and on research projects that they finance. The initiative has been crucial to put Alzheimer's and dementia higher at the global political agenda, include the WHO. The March 2015 event will be essential to estbalish more results and guarantee a sustainable follow up and initiatives beyond the G7 countries.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.056 | 0.022 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".