Participant Perspectives on Housing and Landlords in a Canadian Housing First Program
Bibliographic record
Abstract
Housing First (HF) is an evidence-based approach to housing and services for adults who are chronically homeless and have a psychiatric disability. Research has demonstrated that HF rapidly ends homelessness but less in known about how participants experience their housing environments and landlords. This study is a part of a larger Canadian randomized field trial of HF that included qualitative interviews with participants in five cities. The narratives of 127 participants randomized to HF (n=82) or Treatment as Usual (TAU, n=45) were collected with regard to their perceptions of housing and landlords. Participant narratives were analyzed using thematic analysis and quantitative comparison of qualitative results. Analysis revealed that HF participants were four times more likely to describe feeling safe in their housing than TAU participants. Additionally, participants across treatment groups described being unsure of their tenancy rights and responsibilities and described experiences of surveillance. Descriptions of surveillance differed qualitatively between groups with HF participants describing personal surveillance and TAU participants describing impersonal surveillance. It was observed that women and Aboriginal participants had unique challenges related to safety and surveillance in HF programs. Implications for the implementation of HF programs are discussed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".