Biophilic Design Strategies in Long-Term Residential Care Environments for Persons with Dementia
Bibliographic record
Abstract
The number of persons living with dementia and related cognitive disorders is predicted to increase dramatically in the coming years. As a consequence, the need is increasing for appropriately designed long-term care (LTC) environments and design guidelines for these settings. This investigation presents the findings of a broad literature review on biophilic design and its application to a set of LTC architectural case studies selected for the degree to which each variously expresses key attributes of a set of ten biophilic patterns particularly rooted in the day to day experience of the aged in these care settings: visual connections with nature, non-visual connection with nature, non-rhythmic sensory stimuli, thermal and airflow variability, presence of water, dynamic and diffused light, complexity and order, prospect, refuge, and mystery. The three methodological aims are to conduct an in-depth literature review, to distill the aforementioned subset of biophilic patterns with respect to how the aged experience their built surroundings, and third, to examine these in light of their various expression in recently built state-of-the art LTC settings for persons with dementia and related cognitive disorders. Residents’ engagement with and proximity to nature and landscape, and transactions with biophilia-inspired artifacts was the principal focus. The case studies are further examined in relation to the planning and design of LTC environments in the context of the COVID-19 pandemic. Future biophilic-inspired directions for evidence-based research and design for persons with dementia and related cognitive disorders are discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".