Scoping review of dementia primary prevention policies in England: do they balance reach and agency?
Bibliographic record
Abstract
Objectives: To ascertain the balance of dementia risk reduction policies in England, considering their reach (population-wide vs targeted at specific individuals) and agency (the level of resource required to benefit from the intervention). Design: Scoping review. Data sources: Academic databases (Medline, the Health Management Information Consortium and Overton) and the webpages of relevant national and local government agencies and associated bodies (including: the UK Government, the UK Health Security Agency, National Health Service England, National Institute for Health and Care Excellence and local governments and healthcare organisations from the East of England region) were searched. Eligibility criteria: Any written documents or service webpages from, or endorsed by, governmental organisations or arms-length bodies which describe, recommend or evaluate current or formally proposed interventions for the reduction or control of one or more modifiable risk factors for dementia were included. Policies targeted at people with existing cognitive impairment and/or dementia were excluded. Data extraction and synthesis: Data on policy description, reach and agency were extracted from identified dementia primary prevention policy documents by one author. Policies common to several organisations were grouped, and then synthesised across risk factor group and by tier of government. The numerical balance of policies (between axes of reach and agency) was compared across risk factor group and current policy/proposed status. Results: From a total of 8210 hits, 366 policy documents were included. From these, 79 distinct policies were identified, targeted at dementia (n=3), cardiovascular health (n=23), smoking and alcohol (n=17), depression and social isolation (n=12), air pollution (n=10), low formal education (n=9), hearing impairment (n=3) and traumatic brain injury (n=2). Overall, 67.1% (53/79) of current policies had population-reach, 53.2% (42/79) were considered low-agency and 39.2% (31/79) were both population-reach and low-agency. Conclusions: There is currently a policy balance between population-reach and targeted-reach, and high-agency and low-agency interventions, for dementia risk reduction in England. However, a predominance of population-reach, low-agency interventions may be required to match the scale of the challenge and improve equity.
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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 | 0.135 | 0.441 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.026 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".