A Framework for Designing Spatial Orientation Tools to Enhance the Experiences of Persons Living with Dementia
Bibliographic record
Abstract
This paper introduces a framework aimed at guiding the design of orientation and wayfinding tools to support people living with dementia. As spatial disorientation and impaired wayfinding are common challenges faced by individuals living with dementia, especially in unfamiliar environments such as care homes, addressing these design needs is essential to improve autonomy and quality of life (QoL). This study presents the initial phase of a case study examining a Respite House in Ontario, Canada, and shares findings from a qualitative literature review that informed the creation of a design framework: Orientation Design to Support Persons Living with Dementia . The review synthesizes current design strategies, accessibility considerations, and environmental factors related to wayfinding and orientation to support more inclusive, responsive care environments. The framework is structured around four key categories: Proximal Factors, Building Codes, Design Principles and Approaches, and Environmental Factors. Each category consists of design elements which have been identified as important to assist care providers and designers to enhance the wayfinding experiences of individuals living with dementia, in support of their well-being.
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 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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".