Dementia care and prevention in community settings: a built environment framework for cognitive health promotion
Bibliographic record
Abstract
PURPOSE OF REVIEW: Most people with dementia live in the community. As lifespan increases, one in three persons aged 85+ are expected to live with dementia. We conduct a systematic search to identify frameworks for dementia care and prevention in community settings. This is important to ensure quality of life for people living with cognitive decline (PLCD). RECENT FINDINGS: 61 frameworks are synthesized into the dementia care and prevention in community (DCPC) framework. It highlights three levels of provision: built environment and policy supports, access and innovation, and inclusion across stages of decline. Domains of intervention include: basic needs; built environment health and accessibility; service access and use; community health infrastructure; community engagement; mental health and wellbeing; technology; end-of-life care; cultural considerations; policy, education, and resources. Personhood is not adequately represented in current built environment frameworks. This is supplemented with 14 articles on lived experiences at home and social practices that contribute to PLCD's social identity and psychological safety. SUMMARY: Policy makers, health and built environment professionals must work together to promote "personhood in community" with PLCD. Clinicians and community staff may focus on inclusion, social identity and a sense of at-homeness as attainable outcomes despite diagnosis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".