Creating dementia friendly community for social inclusion: A scoping review
Bibliographic record
Abstract
Abstract Background Social exclusion is commonly reported by people with dementia and their families. Dementia‐friendly community has emerged as an idea that holds potential to contribute to the mitigation of social exclusion. The objective of the scoping review is to identify strategies of creating dementia‐friendly communities that support people with dementia and their informal care providers. Method This scoping review follows the Joanna Briggs Institute scoping review methodology and takes place between March and May, 2020. The review included studies based in community settings with participants living at home with early to late stages of dementia and their families. It included a three‐step search strategy: (1) to identify keywords from CINAHL & AgeLine; (2) to conduct a second search using all identified keywords and index terms across selected databases (CINAHL, AgeLine, MEDLINE, PsycINFO, Web of Science, ProQuest and Google); and (3) to hand‐search the reference lists of all included articles and reports for additional studies. Result A total of 28 papers were included in the review. Content analysis identified strategies of creating dementia‐friendly communities: (a) active involvement of people with dementia and carers, (b) inclusive environmental design, (c) public education to reduce stigma and raise awareness, (d) customized strategies informed by dementia‐friendly and inclusive theories. Conclusion This scoping review provides an overview of current evidence on strategies that support dementia‐friendly communities for social inclusion. The findings offer insights to inform strategies for education, practice, policy and future research.
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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 | 0.031 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 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".