Adapting Homelessness Interventions for People Who Use Drugs or Alcohol in Montreal, Quebec: Service User Perspectives
Bibliographic record
Abstract
Background: Under a housing affordability crisis, Montreal, Canada, is host to a growing homeless population. While people who use drugs or alcohol make up a large part of this group, homeless resources in the city continue to exclude them through sobriety rules or by not adapting programming to their specific needs. This systematic exclusion, and the conditions of these resources, can often be retraumatizing for individuals seeking help. Applying a trauma‐informed spaces of care framework, this research asks what are the needs of homeless individuals who use substances to exit homelessness? What are the current limits within homeless resources in Montreal to actualize these needs? How can they change to meet these needs? Methods: In 2023, 30 semistructured interviews were conducted, with follow‐up at 3 months, with individuals who use drugs or alcohol currently experiencing homelessness. Transcribed interviews were analyzed in Nvivo. Results: Findings called for serious reforms to homeless service provision, with an emphasis on more forms of harm reduction‐based programming, integrated occupational activities, improved psychosocial accompaniment, better division of service users, and alternative and adapted housing interventions for substance users. Most participants disclosed potentially traumatic life experiences, highlighting the need for trauma‐informed programming. Conclusion: Allowing individuals to articulate their needs and desires for programming demonstrates that this group recognizes the inadequacy of services and identifies the homeless resource as a site of potential traumatization. While the recommendations of people with living experience of homelessness and substance use articulate promising practices in substance use recovery, as well as homelessness service provision, homeless service providers are slow to adapt their programming accordingly.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".