Protocol for A Mixed-Methods Study: Dementia-Inclusive Streets and Community Access, Participation, and Engagement (DemSCAPE)
Bibliographic record
Abstract
Neighbourhoods are known to help maintain functional abilities and enable out-of-home activities and social participation for people living with dementia. Dementia friendly and inclusive communities (DFC) frameworks recognize the importance of developing supportive and empowering environments for people living with dementia and their families. Beyond the core objective of raising awareness and eliminating stigma associated with dementia, most DFC frameworks also focus on improving accessibility and navigability in the neighbourhood environment. Limited research on this topic points to the importance of evidence-based design of the neighbourhood built environment to promote mobility and wayfinding, legibility, familiarity, and safety for people living with dementia. Increased relevance of DFCs for policy and practice calls for expanding this body of knowledge. The proposed study, titled “Dementia-inclusive Spaces for Community Access, Participation, and Engagement (DemSCAPE),” focuses on identifying neighbourhood destinations considered important by people living with dementia, as well as neighbourhood built environmental features relevant for their outdoor mobility, engagement, and social participation. This study protocol paper offers key information on 1) the need for mixed methods research on this topic and its theoretical and methodological underpinnings, 2) study sampling and recruitment strategy, 3) data collection methods, which include a series of structured and semi-structured sit-down interviews and a walk-along interview, 4) procedure for data analysis, 5) ethical and methodological considerations, and 6) measures taken to enhance study rigour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".