Social determinants of dementia: A scoping review
Bibliographic record
Abstract
Dementia risk is influenced by the social conditions in which people live. These social determinants of dementia (SDOD) are potential targets for prevention. We conducted a comprehensive scoping review to identify current evidence on SDOD in two stages: (1) review of systematic reviews on SDOD; and (2) review of primary literature to address identified gaps in the evidence base. Of the 3445 articles screened, 26 reviews and 74 primary studies were included. Evidence from reviews provide clear and consistent patterns for some SDOD (e.g., education, air pollution, socioeconomic status, ethnicity), while evidence for others is still emerging (e.g., housing quality/stability), or lacking entirely (e.g., incarceration). SDOD are important over the life course. Additional evidence is needed for understudied domains and to unravel the complex interactions between determinants, whilst education and air pollution stand out as key targets for public health interventions. HIGHLIGHTS: There is clear and consistent evidence for some social determinants of dementia (SDOD) across diverse domains. Evidence for other SDOD (e.g., housing, incarceration) is still emerging or lacking. Evidence for SDOD comes mainly from high-income countries. The complex intercorrelations between SDODs demand a nuanced analytical approach. Standardized measures and longer follow-up to study SDOD are recommended.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".