Creating Dementia-Friendly Communities for Social Inclusion: A Scoping Review
Bibliographic record
Abstract
Aims: This scoping review explores key strategies of creating inclusive dementia-friendly communities that support people with dementia and their informal caregiver. Background: Social exclusion is commonly reported by people with dementia. Dementia-friendly community has emerged as an idea with potential to contribute to cultivating social inclusion. Methods: This scoping review follows the Joanna Briggs Institute scoping review methodology and took place between April and September 2020. The review included a three-step search strategy: (1) identifying keywords from CINAHL and AgeLine; (2) conducting 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) hand-searching the reference lists of all included articles and reports for additional studies. Results: Twenty-nine papers were included in the review. Content analysis identified strategies for creating dementia-friendly communities: (a) active involvement of people with dementia and caregivers (b) inclusive environmental design; (c) public education to reduce stigma and raise awareness; and (d) customized strategies informed by theory. Conclusion: This scoping review provides an overview of current evidence on strategies supporting dementia-friendly communities for social inclusion. Future efforts should apply implementation science theories 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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".