An International Environmental Scan of Social Housing for Older Adults
Bibliographic record
Abstract
Abstract The City of Toronto is creating a standalone housing corporation to focus on the specific needs of low-income older adults living in social housing. A key focus of this new corporation will be to provide housing, health and community support services needed to optimize older adult tenants’ ability to maintain their tenancy and age in place with dignity and in comfort. To support this work, we conducted an environmental scan of service delivery models that connect low-income older adults living in social housing with health and support services. Desktop research was undertaken to identify relevant programs. For each model, key details were extracted including housing type, services offered, provider information, rent structure and funding sources. The scan identified 34 examples of social housing programs for older adults run by public, private and non-profit agencies across Canada, the United States and Europe that integrated health and supportive services. Successful models were those that understood the needs of tenants and developed collaborative partnerships with health and social service providers to create flexible place-based programs. A common challenge across jurisdictions was privacy legislation that made it difficult to share health and tenancy data with program partners. The presence of on-site staff that focused on building trust and community among tenants was considered key for identifying tenants requiring additional supports in order to age in place. These insights offer important considerations on how integrated supportive housing service models promote housing stability and support better health and wellbeing among older adults residing in social housing.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".