Towards A Dementia-friendly Built Environment: Wayfinding Systems to Support Persons with Dementia in Geriatric Psychiatry Units Toronto Rehabilitation Institute
Bibliographic record
Abstract
The aim of the study was to generate informed design recommendations for Geriatric Psychiatry Units in hospitals in order to create and facilitate a dementia friendly built environment with accessible inclusive wayfinding systems. In establishing a set of design guidelines to achieve this outcome, three sources of knowledge and practice were drawn together: preliminary observations; interviews with the staff of the GPU unit at the Toronto Rehabilitation Institute; and a meta-ethnography study. The results show that simple design modifications with properly designed floor layout may have a significant impact on residents’ behavioural outcomes; such as using landmarks, cues, colour schemes, and dementia-friendly signage system. The guidelines of the research indicate and argued that wayfinding systems have to be designed and based on the particular environmental responses of the residents, \nmaking these systems more readily accessible and inclusive for the diversity of resident population including their abilities and background. Of particular importance, is that \nresearch points to the need and potential for designing environments with home-based social activities in mind; like laundry folding, cleaning dishes, green table to play cards, and old fashioned office desks, etc. Recommendations established to address these needs are relatively low-cost for GPU units, and may be extendable to other built environments outside of hospital settings.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".