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Record W7135089283 · doi:10.3929/ethz-c-000794805

Examining the prevention approach in National Dementia Plans from European and North American countries

2025· other· en· W7135089283 on OpenAlexaboutno aff
Mattia Andreoletti, Alessandro Blasimme

Bibliographic record

VenueRepository for Publications and Research Data (ETH Zurich) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaIdentification (biology)Public healthSample (material)Relation (database)Executive summaryQualitative research

Abstract

fetched live from OpenAlex

Objectives This paper aims to provide a comprehensive review of National Dementia Plans (NDPs) from selected European and North American countries, focusing on the distinct prevention strategies outlined and the approaches employed for reducing dementia risk. Method The sample consisted of 16 NDPs from Austria, Canada, Finland, France, Germany, Greece, Ireland, Italy, Liechtenstein, Luxembourg, Malta, the Netherlands, Spain, Switzerland, the UK, and the USA. These NDPs were retrieved from the Alzheimer's Disease International (ADI) database, with regular updates checked on official governmental websites. A qualitative analysis was conducted to identify common themes related to the vision, goals, and corresponding actions and measures within these strategies. Results Our analysis revealed that dementia prevention is a strategic goal for most of the countries studied. Common actions identified include the identification of risk factors, advancing research, promoting healthy aging, increasing public awareness, and encouraging lifestyle interventions. Conclusion We discuss the limitations and challenges of these actions, and more broadly, of the NDPs in relation to the recent literature on the most effective approaches to preventing dementia. We suggest adopting a more “horizontal” approach to dementia prevention, which current NDPs overlook in favor of “vertical” paradigms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.263
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.170
GPT teacher head0.379
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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