Rediscover Neighborhood: A Synthesis of Lived Experiences Following Dementia Diagnosis Through Walking Interview
Bibliographic record
Abstract
Abstract Around 85% of older adults with dementia in the US reside within the community (NHATS, 2019). However, many do not venture far from their homes due to fears of getting lost or the loss of their driving ability. Therefore, the immediate neighborhood environment plays a crucial role in supporting their independence. Walking interview is an effective in-situ, participatory method to capture the lived experiences and environmental interactions of persons with dementia (PwDs), yet studies on walking interviews with PwDs is often limited by small sample sizes and a narrow focus. A systematic review and meta-synthesis can bridge these gaps and offer a critical, comprehensive view of neighborhood experiences after dementia diagnoses. We included 17 papers that involved 172 PwDs from four countries. Guided by the Contexts for Development and Aging (CODA) framework, we found that environmental features (e.g., street crossings, sidewalks, and landmarks) can either support or challenge PwDs’ outdoor mobility, yet many adapt by developing unique strategies, modifying daily routines, or relying on others. From the social perspective, PwDs form a vital, reciprocal network with family, friends, neighbors, and even familiar “strangers”. Technology further expands PwDs’ physical and social experiences by facilitating location tracking and virtual interactions, despite potential accessibility barriers and misuse. The study revealed how PwDs address vulnerability and adapt with resilience in neighborhoods as dementia progresses. More research is needed on the neighborhood experiences of PwDs, focusing on translating their daily realities into actionable frameworks for dementia-friendly communities.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".