Reducing the Risk of Cognitive Decline and Dementia: WHO Recommendations
Bibliographic record
Abstract
With population ageing worldwide, dementia poses one of the greatest global challenges for health and social care in the 21st century. In 2019, around 55 million people were affected by dementia, with the majority living in low- and middle-income countries. Dementia leads to increased costs for governments, communities, families and individuals. Dementia is overwhelming for the family and caregivers of the person with dementia, who are the cornerstone of care and support systems throughout the world. To assist countries in addressing the global burden of dementia, the World Health Organisation (WHO) developed the Global Action Plan on the Public Health Response to Dementia 2017-2025. It proposes actions to be taken by governments, civil society, and other global and regional partners across seven action areas, one of which is dementia risk reduction. This paper is based on WHO Guidelines on risk reduction of cognitive decline and dementia and presents recommendations on evidence-based, multisectoral interventions for reducing dementia risks, considerations for their implementation and policy actions. These global evidence-informed recommendations were developed by WHO, following a rigorous guideline development methodology and involved a panel of academicians and clinicians with multidisciplinary expertise and representing geographical diversity. The recommendations are considered under three broad headings: lifestyle and behaviour interventions, interventions for physical health conditions and specific interventions. By supporting health and social care professionals, particularly by improving their capacity to provide gender and culturally appropriate interventions to the general population, the risk of developing dementia can be potentially reduced, or its progression delayed.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".