Perspectives on a transitional housing program for people who use substances who experience homelessness and live with a mental health issue: a pilot study in an urban northern city in Canada
Bibliographic record
Abstract
BACKGROUND: A new transitional housing program was established in Sudbury, Ontario, Canada, in response to the escalating prevalence of substance use and homelessness, and the specific challenges faced in Northern Ontario. There is a scarcity of research investigating the implementation of housing programs with clinical, social, and functional support for people with substance use in Northern settings in the era of Fentanyl and its analogs and a contaminated dangerous drug supply. To bridge this knowledge gap, we evaluated a unique harm reduction-focused transitional housing program. Our study objectives encompassed exploring patients' viewpoints on considerations when implementing a transitional housing program for people with substance use and mental disorders. METHODS: This is a qualitative interview study of 12 clients from a transitional housing program that offers comprehensive health and social assistance through addiction medicine, psychiatric consultation, primary care, and harm reduction services to clients in a transitional housing program in an Urban setting in Northern Canada. This study was a pilot project, to gather their perspectives on the care provided by the team. Maslow's Hierarchy of Needs was used to contextualize the data. RESULTS: Participants emphasized the importance of the program's housing first approach, its positive impact on accessing health and social services, and the increase in sense of belonging, self-esteem, and confidence because of being in the program. Participants also indicated that the program had a positive overall impact, leading to reduced substance use, improved mental and emotional well-being, and enhanced socio-economic conditions. However, several considerations were highlighted as important for ensuring the effectiveness of the program, such as better aligning client and program expectations, facilitating access to community supports with food, support with medication regimen, providing empathetic engagement, and individualizing approaches to care. CONCLUSION: A new transitional housing program in a Northern Urban setting in the era of an increasingly contaminated drug supply led to perceived positive outcomes for clients. Important considerations include focusing (or enhancing supports) on physiological needs and empathetic, individualized approaches to care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".