Forces et défis des différents modèles de logement permanent avec soutien : perspective des organisations œuvrant dans le secteur au Québec
Bibliographic record
Abstract
OBJECTIVES: Permanent supportive housing (PSH) is the preferred strategy for eradicating homelessness. This study seeks to outline the specificities of PSH in Montreal (Quebec, Canada), to compare the different models and highlight their respective strengths and challenges. METHOD: Data was collected in 2023 through 31 organizations from eight governmental bodies or Quebec associations and 23 Montreal PSH resources. A sample of 42 managers and practitioners from the homelessness/housing sector participated by completing an interview and/or a questionnaire. The study used a mixed-methods approach integrating descriptive and content analyses. RESULTS: Community-based PSH was the most prevalent model, although half of the resources offered both community-based and private-sector PSH. A median of 70 residents received support, with only one-third of those being followed at least once a week. Common challenges were mainly linked to funding (e.g., quality affordable housing) or due to the complexity of providing follow-up to residents. Key distinctions were based on whether the housing was contracted in the private sector, and on whether support was available onsite. Challenges specific to private-sector PSH included relationships with landlords, the remoteness of follow-up sites, and resident isolation. Community-based and social PSH faced issues associated with adherence to house rules and resident stigmatization. CONCLUSION: Findings indicate that increasing the number of resources, the intensity and diversity of support, and expanding relocation options would be beneficial to residents.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 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".