HOUSING AND COMMUNITY FOR OLDER ADULTS AS AGING IN PLACE (AIP) ENABLER: AN INTERNATIONAL PERSPECTIVE
Bibliographic record
Abstract
Abstract This symposium discusses various approaches from five different countries that enable Aging in Place (AIP) through housing and community. The achievement of AIP depends on the institutional and cultural context. A study from New Zealand focuses on making new small homes accessible, and shows while accessible design is considered good design, not everyone understands it. User-centered accessible design is not commonly used, and trade-offs exist between accessibility, affordability, and size. A Korean study examines loneliness in older women living alone in a rural senior cohousing community. The findings show senior cohousing helps protect against loneliness by serving as a community hub for gathering, sharing, and caring. A Japanese study found “IBASHO” activities (i.e., place-based specific activities) help promote permanent community living in marginalized communities at higher risk of depopulation. Participating in such activities makes residents feel at ease in their community, even if they live alone or have poor health. A Canadian study discussed the development of a Naturally-Occurring Retirement Community (NORC) in Vancouver, formed by East Asian older adults who could not travel abroad due to border closures during the pandemic. The study explores the health-promoting nature of mutuality and its potential for population-level spillover effects on community well-being beyond the direct reach of formal programs. The last study from Hong Kong found significant improvements in perceived age-friendliness, with the greatest improvements among low-income older adults. The empirical knowledge from these studies offers valuable insights into a contextually rich understanding of a wide range of housing and communities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".