“A place to be safe, feel at home and get better”: including the experiential knowledge of potential users in the design of the first wet service in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: The harmful use of alcohol is one of the leading health risk factors for people's health worldwide, but some populations, like people who experience homelessness, are more vulnerable to its detrimental effects. In the past decades, harm reduction interventions that target these complex issues has been developed. For example, wet services include a wide range of arrangements (wet shelters, drop-in centers, transitory housing, etc.) that allow indoor alcohol use and Managed Alcohol Programs provide regulated doses of alcohol in addition to accommodation and services. Although the positive impacts of these interventions have been reported, little is known about how to integrate the knowledge of people experiencing homelessness and alcohol dependence into the design of such programs. The aim of this study is to present the findings of such an attempt in a first wet service in Montreal, Canada. METHODS: Community based participatory research approach and qualitative methods-including semi-structured interviews and focus groups-were used to collect the knowledge of potential users (n = 34) of the wet service. The data collected was thematically analyzed. RESULTS: Participants reported experiencing harsh living conditions, poverty, stigmatization and police harassment, which increased their alcohol use. The intersection between participants' alcohol dependence and homelessness with the high barriers to access public services translated into their exclusion from several of such services. Participants envisioned Montreal's wet service as a safe space to drink, a place that would provide multiple services, a home, and a site of recovery. CONCLUSIONS: Integrating the knowledge of potential users into the design of harm reduction interventions is essential to develop better and more adapted services to meet complex needs. We propose that it could fosters users' engagement and contribute to their sense of empower, which is crucial for a group that is typically discriminated against and suffers from marginalization.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".