Scoping review of managed alcohol programs
Bibliographic record
Abstract
BACKGROUND: Internationally, strategies focusing on reducing alcohol-related harms in homeless populations with severe alcohol use disorder (AUD) continue to gain acceptance, especially when conventional modalities focused on alcohol abstinence have been unsuccessful. One such strategy is the managed alcohol program (MAP), an alcohol harm reduction program managing consumption by providing eligible individuals with regular doses of alcohol as a part of a structured program, and often providing resources such as housing and other social services. Evidence to the role of MAPs for individuals with AUD, including how MAPs are developed and implemented, is growing. Yet there has been limited collective review of literature findings. METHODS: We conducted a scoping review to answer, "What is being evaluated in studies of MAPs? What factors are associated with a successful MAP, from the perspective of client outcomes? What are the factors perceived as making them a good fit for clients and for communities?" We first conducted a systematic search in PubMed, Embase, PsycINFO, CINAHL, Sociological Abstracts, Social Services Abstracts, and Google Scholar. Next, we searched the gray literature (through focused Google and Ecosia searches) and references of included articles to identify additional studies. We also contacted experts to ensure relevant studies were not missed. All articles were independently screened and extracted. RESULTS: We included 32 studies with four categories of findings related to: (1) client outcomes resulting from MAP participation, (2) client experience within a MAP; (3) feasibility and fit considerations in MAP development within a community; and (4) recommendations for implementation and evaluation. There were 38 established MAPs found, of which 9 were featured in the literature. The majority were located in Canada; additional research works out of Australia, Poland, the USA, and the UK evaluate potential feasibility and fit of a MAP. CONCLUSIONS: The growing literature showcases several outcomes of interest, with increasing efforts aimed at systematic measures by which to determine the effectiveness and potential risks of MAP. Based on a harm reduction approach, MAPs offer a promising, targeted intervention for individuals with severe AUD and experiencing homelessness. Research designs that allow for longitudinal follow-up and evaluation of health- and housing-sensitive outcomes are recommended.
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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 | 0.034 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.038 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 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".