Profiles of Permanent Supportive Housing Residents Related to Their Quality of Life and Community Integration
Bibliographic record
Abstract
Permanent supportive housing (PSH) is the main approach advocated in Western countries for eradicating homelessness. Considering that PSH residents are not a homogeneous group and that their quality of life (QoL) and community integration (CI) might differ in this setting, improving our understanding of these residents’ profiles may help stakeholders formulate informed recommendations to improve PSH. This study identified PSH resident profiles based on their QoL, CI, and sociodemographic and clinical characteristics and associated these profiles with housing features and service use. A total of 308 PSH residents were recruited in Montreal (Canada) in 2020–2022. Structured interviews were conducted. PSH resident profiles were produced with cluster analysis and subsequently compared using chi‐square, Fisher’s, and t‐tests, taking into account housing features and service use. Three PSH resident profiles were found. Profile 1 residents (22% of the sample) had low QoL and CI, were younger, and had major social and health issues and unmet needs. Showing moderate QoL and CI, Profile 2 residents (27%) were more educated, had little foster care history, were older on their first homelessness episode, and had few co‐occurring MD‐SUD. Profile 3 residents (51%) had the best QoL and CI and mostly included men with little education, affected by co‐occurring MD‐SUD and satisfied with services. More intensive housing support and care coordination may be recommended for Profile 1 PSH residents in response to their diverse needs. Work integration may be beneficial to Profile 2 residents, with programs such as Individual Placement and Support, along with increased rehabilitation activities. A better integration of MD‐SUD treatments may be promoted for Profile 3 residents. Considering most PSH residents had multiple health issues and unmet needs, satisfaction with care could be monitored better, as it was found to be a key variable in measuring care adequation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".