Exploring the experience of inpatients with severe alcohol use disorder on a managed alcohol program (MAP) at St. Paul’s Hospital
Bibliographic record
Abstract
BACKGROUND: Managed alcohol programs are a harm reduction approach for people with severe alcohol use disorder that provide alcohol in a structured setting. We examined the patient experience of receiving alcohol after the implementation of a hospital-based managed alcohol program. METHODS: Using an interpretative descriptive methodology, we conducted interviews with five patients. The criteria for enrollment included continuation of community managed alcohol program or provision of alcohol for stabilization in hospital and ability to provide consent. RESULTS: Five themes emerged in the analysis: (1) Reasons for alcohol use highlighting factors leading to alcohol consumption; (2) I'm very appreciative indicating participant's perception of hospital-based managed alcohol program; (3) From just vibrating to calm and It's kinda like a pacifier for me recognizing the impact of hospital-based managed alcohol program on managing withdrawal and on psychological health; (4) I have no need to go anywhere at all demonstrating engagement in healthcare; and (5) Might be nice to have a selection for other people indicating the need for a broader selection of alcohol. CONCLUSIONS: This study helped to explore the effectiveness of a hospital-based managed alcohol program as experienced by the patients. Overall, participants had a positive experience on hospital-based managed alcohol program. Their perceptions can be used to inform implementation of managed alcohol programs in other hospital settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".