Implementing managed alcohol programs in hospital settings: A review of academic and grey literature
Bibliographic record
Abstract
ISSUES: People with severe alcohol use disorders are at increased risk of poor acute-care outcomes, in part due to difficulties maintaining abstinence from alcohol while hospitalised. Managed alcohol programs (MAP), which administer controlled doses of beverage alcohol to prevent withdrawal and stabilise drinking patterns, are one strategy for increasing adherence to treatment, and improving health outcomes for hospital inpatients with severe alcohol use disorders. APPROACH: Minimal research has examined the implementation of MAPs in hospital settings. We conducted a scoping review to describe extant literature on MAPs in community settings, as well as the therapeutic provision of alcohol to hospital inpatients, to assess the feasibility of implementing formal MAPs in hospital settings and identify knowledge gaps requiring further study. Four academic and 10 grey literature databases were searched. Evidence was synthesised using quantitative and qualitative approaches. KEY FINDINGS: Forty-two studies met review inclusion criteria. Twenty-eight examined the administration of alcohol to hospital inpatients, with most reporting positive outcomes related to prevention or treatment of alcohol withdrawal. Fourteen studies examined MAPs in the community and reported that they help stabilise drinking patterns, reduce alcohol-related harms and facilitate non-judgemental health and social care. IMPLICATIONS/CONCLUSIONS: MAPs in the community have been well described and research has documented effective provision of alcohol in hospital settings for addressing withdrawal. Implementing MAPs as a harm reduction approach in hospital settings is potentially feasible. However, there remains a need to build off extant literature and develop and evaluate standardised MAP protocols tailored to acute-care settings.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".