Advancing home-like environments for memory care residents within nursing homes
Bibliographic record
Abstract
Abstract Objective: This article reports on 5 specialized memory care environments within nursing homes in Canada through the theory of affordances with the aim of understanding the layered implications of affordances on memory care residents by curating objects within and configuring or designing spatial environments. Methods: A spatial/object-centric approach was taken by using a detailed analysis framework based on a robust interpretation of the theory of affordances and well-known elements, principles, and physical/construction properties of interior and spatial design. A web content analysis method, using hundreds of photographs, drawings, and textual information belonging to 5 nursing homes posted on websites and on social media, was used. Results: The results include a detailed analysis framework informed by affordance theory and 3 themes that reveal details about the designed environments. The 3 themes are: (1) how contextual factors of affordances of place and care played out, (2) how physical, cultural, and semantic affordances aided or detracted from memory care, and (3) how home-like environments with public, semiprivate, and private spaces involved multiple affordances and constraints that provided multisensory clues towards supporting and/or limiting memory care residents’ possible actions. Conclusions: We conclude that although affordances can open a range of possible actions, they are not ideal for configuring or designing home-like environments, and it is necessary for memory care residents to be presented with constraints that limit alternatives and misaffordances. This article provides evidence about how affordances and constraints are (and could be) intentionally embedded in home-like memory care environments in nursing homes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".