Promoting Housing Stability Through Eviction Prevention for Older Adults in Social Housing: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Evictions are a major contributor to homelessness among low-income older renters, many of whom are living in social housing. Research indicates that social housing may protect individuals from eviction, but older adults may still be vulnerable, especially for non-payment of rent. This research used a qualitative approach to understand the factors that place older adults in social housing at risk of eviction and identify strategies to promote housing stability. RESEARCH DESIGN AND METHODS: Participants included older adult tenants in social housing in Toronto, Canada (n=58) as well as community-based health and social service providers (n=58) that operate in the buildings. Semi-structured interviews and focus groups explored (a) experiences with eviction; (b) challenges tenants face managing their tenancy; and (c) supports tenants need to maintain their housing. RESULTS: Most service providers had experiences supporting a tenant under threat of eviction. Tenants similarly had experiences with the eviction process, ranging from being threatened with a future eviction to receiving eviction notices and attending hearings with the landlord and tenant board. To understand experiences with evictions and opportunities to strengthen eviction prevention practices, we generated the following themes: (a) creating fear and mistrust through evictions; (b) identifying the underlying cause of an eviction; (c) ineffective tenancy management practices; and (d) proactive community supports. DISCUSSION AND IMPLICATIONS: Current eviction prevention strategies were viewed as inadequate, and findings highlighted the need to transform supports to better meet the needs of low-income older tenants. This includes more proactive and "senior friendly" approaches and increased access to community support services to promote housing stability.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".