Aging in the Right Place for Older Adults Experiencing Housing Insecurity: An Environmental Assessment of Temporary Housing Program
Bibliographic record
Abstract
Research on programs offering senior-specific housing supports and enabling “aging in the right place” (AIRP) for “older persons with experiences of homelessness” (OPEH) is limited. This paper presents an environmental assessment of a “transitional housing program” (THP) in Metro Vancouver, Canada, for OPEH to AIRP. Data were collected using Aging in the Right Place Environmental (AIRP-ENV) and Secondary Observation (AIRP-ENV-SO) audit tools designed to evaluate multi-unit housing for OPEH. The 241-item AIRP-ENV tool was used to assess the built environmental features of four multi-unit buildings of the THP. The AIRP-ENV-SO tool was used to collect contextual data on the function, safety, and land use of the surrounding neighborhood. Findings identified built environment and urban design features that support THP residents’ safety, security, accessibility, functionality, social activity, autonomy, and identity. The THP buildings were rated ‘Good’ for accessibility, functionality, autonomy and identity, while ‘Satisfactory’ or ‘Poor’ for safety, security, and social activity. Findings point to the built environmental features (e.g., size and layout of spaces) required in the THP to create opportunities for increased social engagement among residents and enhanced safety and security. The AIRP-ENV and AIRP-ENV-SO audit tools can help inform programs across the housing continuum to develop supportive built environments that promote AIRP for OPEH.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".