Narratives of people with serious mental illness in Housing First: a qualitative analysis of factors contributing to housing instability and housing stability
Bibliographic record
Abstract
There has been little previous research that has examined factors related to housing stability and instability among Housing First (HF) tenants. Qualitative research was used to determine themes related to housing stability and instability for people with mental illness participating in a HF program implemented in five Canadian cities. Data were gathered for all participants who completed qualitative interviews at baseline and an 18-month follow-up. At the 18-month follow-up interviews, those who were stably housed (n = 110) were significantly more likely to report positive life changes than those who were unstably housed (n = 75). Among a sub-sample of these participants, in-depth narratives were compared for stably housed (n = 25) and unstably housed (n = 21) tenants. Challenges to achieving housing stability included substance use and continued exposure to substance-using networks, evictions/multiple housing losses, incarceration and/or involvement with the legal system, and neighborhood location of housing. Themes promoting housing stability were having positive relationships with the HF program staff and program, achieving greater community integration, and making progress towards recovery. Recommendations for promoting housing stability were provided.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".