USING PHOTOVOICE TO EXPLORE THE SALIENCY OF NEIGHBORHOOD LANDMARKS FOR PERSONS LIVING WITH DEMENTIA
Bibliographic record
Abstract
Abstract This study demonstrates the potential of Photovoice, a participatory action research method involving participant-generated photo-elicitation, to explore how persons living with dementia (PLWDs) perceive neighborhood landmarks. Previous research has highlighted the role of well-designed, stable geographical landmarks in improving the navigability of neighborhoods for PLWDs. However, the specific attributes that render landmarks salient have not yet been sufficiently explored, resulting in inadequate evidence-based environmental design guidelines for dementia-friendly communities (DFCs). To address this gap, a Photovoice study was conducted with five community-dwelling PLWDs and their care partners, as part of a dementia-friendly neighborhood walking program in the city of Seattle, USA. Photovoice facilitated the exploration of saliency of neighborhood landmarks from an emic perspective by empowering PLWDs to identify and take photos of salient landmarks during the group walk and interpret and reflect on attributes that contributed to saliency using the photos as visual aids in a focus group discussion and survey questionnaire. PLWDs associated the saliency of landmarks not only with objective physical attributes, e.g., size, shape, color, texture, but also with subjective factors linked to their past, passions, hobbies, and emotions related to having dementia. Findings suggest that the design of outdoor landmarks should satisfy universal design principles, as well as aspects of familiarity, recognizability, and memorability, to ensure that the neighborhood physical environment provides navigational support to PLWDs. The study proposes using Photovoice to facilitate community engagement in the planning and design of DFCs and mobilize people’s lived experience to generate more robust dementia-friendly environmental design guidelines.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".