“There is a Place”: impacts of managed alcohol programs for people experiencing severe alcohol dependence and homelessness
Bibliographic record
Abstract
BACKGROUND: The twin problems of severe alcohol dependence and homelessness are associated with precarious living and multiple acute, social and chronic harms. While much attention has been focused on harm reduction services for illicit drug use, there has been less attention to harm reduction for this group. Managed alcohol programs (MAPs) are harm reduction interventions that aim to reduce the harms of severe alcohol use, poverty and homelessness. MAPs typically provide accommodation, health and social supports alongside regularly administered sources of beverage alcohol to stabilize drinking patterns and replace use of non-beverage alcohol (NBA). METHODS: We examined impacts of MAPs in reducing harms and risks associated with substance use and homelessness. Using case study methodology, data were collected from five MAPs in five Canadian cities with each program constituting a case. In total, 53 program participants, 4 past participants and 50 program staff were interviewed. We used situational analysis to produce a series of "messy", "ordered" and "social arenas" maps that provide insight into the social worlds of participants and the impact of MAPs. RESULTS: Prior to entering a MAP, participants were often in a revolving world of cycling through multiple arenas (health, justice, housing and shelters) where abstinence from alcohol is often required in order to receive assistance. Residents described living in a street-based survival world characterized by criminalization, unmet health needs, stigma and unsafe spaces for drinking and a world punctuated by multiple losses and disconnections. MAPs disrupt these patterns by providing a harm reduction world in which obtaining accommodation and supports are not contingent on sobriety. MAPs represent a new arena that focuses on reducing harms through provision of safer spaces and supply of alcohol, with opportunities for reconnection with family and friends and for Indigenous participants, Indigenous traditions and cultures. Thus, MAPs are safer spaces but also potentially spaces for healing. CONCLUSIONS: In a landscape of limited alcohol harm reduction options, MAPs create a new arena for people experiencing severe alcohol dependence and homelessness. While MAPs reduce precarity for participants, programs themselves remain precarious due to ongoing challenges related to lack of understanding of alcohol harm reduction and insecure program funding.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".