ASSESSING THE INTEGRATION OF AGE-FRIENDLY DESIGN RESEARCH INTO THE DESIGN OF HEALTH CARE ENVIRONMENTS
Bibliographic record
Abstract
Abstract Older adults account for roughly 2/3rds of all patient days in hospitals, and more than 90% of days in post-acute care settings. It is not just the care delivered by clinicians that determines outcomes. Different environments – organizational, social, as well as physical – also have significant impacts on a variety of health and well-being outcomes. Great strides have been made over the past two decades in the design of residential and care environments for older adults, and in the research that evaluates these designs. Research has examined these environments at many scales from overall design concepts (for example, the medical model versus residential design approaches) down to details such as the design of supportive handrails and hardware. This presentation will give a brief overview of this research and explore SAGE (Society for the Advancement of Gerontological Environments) Federation’s post-occupancy process for evaluating residential and care environments for older adults, which has been conducted annually for the past 20 years. However, much less focus has been paid to making other healthcare environments, particularly acute care hospitals, more age-friendly. The Island Health system in Vancouver, Canada, set out to change this and designed the Royal Jubilee Hospital to be age-friendly from pre-admission through discharge. The presentation will also explore the process that Island Health used for decision-making, and how this has influenced subsequent new construction.
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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 | 0.061 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".