A managed alcohol program in Sydney, Australia: Acceptability, cost‐savings and non‐beverage alcohol use
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Managed alcohol programs (MAPs) are a novel harm reduction intervention for people who experience long-term homelessness and severe long-term alcohol dependence. MAPs provide regulated amounts of alcohol onsite under supervision. Preliminary international evidence suggests that MAPs are associated with improvements such as reduced non-beverage alcohol consumption and decreases in some alcohol-related harms. There are currently no MAPs in Australia. We aimed to assess the feasibility of a MAP in inner-Sydney. DESIGN AND METHODS: A survey among eligible homeless alcohol-dependent residents of an inner-Sydney short-stay alcohol withdrawal service occurred in 2014 to assess acceptability. Administrative data were analysed to ascertain estimates of cost-savings for a MAP based in Sydney. RESULTS: Fifty-one eligible participants were surveyed. More than one-quarter (28%) reported consumption of non-beverage alcohol. A residential model received greatest support (76%); the majority (75%) of participants indicated a willingness to pay at least 25% of their income to utilise a MAP. Hospital and crisis accommodation cost-savings were conservatively estimated at AUD$926 483.40 and AUD$347 574.00, respectively per year for a 15-person residential MAP. DISCUSSION AND CONCLUSIONS: Our findings demonstrate the acceptability of a MAP in Sydney among a target population sample, with the implementation of a residential MAP likely to produce significant cost-savings. A trial of a Sydney MAP evaluating the impact on health and social outcomes, including a comprehensive economic evaluation, is strongly recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".