Examining Community-Based Housing Models to Support Aging in Place: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: With the global population aging, there is a demand for older adults to age in place, that is, to live and age well in their home and community with some level of independence. Community-based housing models exist and may support this process. This scoping review aimed to describe and synthesize the ways in which community-based housing models relate to older adults' aging in place and identify strengths and gaps in the literature. RESEARCH DESIGN AND METHODS: The housing models explored were villages, naturally occurring retirement communities, congregate housing and cohousing, sheltered housing, and continuing care retirement communities. This exploratory scoping review examined international peer-reviewed literature published from 2004 to 2019. Six databases were searched using terms related to housing models and older adults. Forty-six articles met the inclusion criteria. Descriptive numerical summary and thematic analysis were used to synthesize study characteristics and findings. RESULTS: Our analysis revealed 4 themes relating to aging in place in the housing models: Social Relations, Health and Well-being, Sense of Self and Autonomy, and Activity Participation. Further analysis identified housing-specific characteristics that appeared to pose barriers to, or enable, aging in place. DISCUSSION AND IMPLICATIONS: To best support aging in place, the findings of the review suggest multiple characteristics worth considering when developing or relocating to a community-based housing model. Further research is required to understand how facilitating characteristics can promote aging in place for community-dwelling older adults.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".