They Do Their Best, but Their Best Isn’t Good Enough: How Social Housing Can Support Older Tenants Aging in Place
Bibliographic record
Abstract
Abstract Toronto Community Housing (TCH) is the second largest social housing landlord in North America, and is home to over 27,000 older adults, half of whom live in 83 “seniors-designated” buildings. There is inadequate and inconsistent delivery of services in these buildings, negatively impacting tenants’ ability to age in place. We conducted two half-day consultations with service providers (n=74) and tenants (n=100) to identify strategies to improve unit condition, promote stable tenancies (i.e., prevent evictions) and enhance access to health and support services for older adults living in TCH. Through facilitated discussion, participants identified their top two recommendations for each priority area and reflected on the strategies that were hardest and easiest to implement, as well as the ones that would have the most and least impact on quality of life for older tenants. Participants recognized the need for more education as a way to empower older tenants and reduce stigma associated with unit condition issues (e.g., pest problems) and arrears. More frequent touch points with tenants was also recommended as a way to identify older adults at-risk of eviction and work proactively (instead of reactively) to support them. Service providers and tenants believed that system navigators working directly in the buildings would be a key facilitator to building trust and helping older tenants access needed services. Outcomes of the have several program and policy implications for TCH, as they partner with the City of Toronto to design a new integrated service model for the seniors-designated buildings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".