A pilot evaluation of managed alcohol programs operating in the context of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Managed Alcohol Programs (MAPs) are a harm reduction strategy designed for individuals with severe AUD, unstable housing, and previous unsuccessful treatment attempts. MAPs provide access to individualized doses of beverage alcohol alongside other social supports and are effective for stabilizing alcohol consumption and reducing alcohol-related harms. In Canada, MAP models (scattered site outreach or fixed site) were developed in response to the COVID-19 pandemic to reduce harms associated with severe AUD, high-risk drinking, and unstable housing as means of supporting physical isolation and distancing. This study provides a description of novel program models and practices and an in-depth description of nine MAP participants in British Columbia in the context of the COVID-19 pandemic. METHODS: This research used a longitudinal mixed methods design. Participants included nine individuals enrolled in MAPs in British Columbia during the COVID-19 pandemic. Quantitative interviews assessing mental and physical health, safety, service usage, substance use, quality of life, well-being, physical distancing and risk behaviours, and alcohol-related harms were collected every 2 weeks for up to 3 months (n = 9). Qualitative interviews about experiences, goals, and expectations related to the MAP were conducted (n = 5). MAP records, including alcohol administration, liver function tests, and healthcare records were collected (n = 8). RESULTS: Clinician-scattered site outreach or fixed-site MAP models were the most common during the COVID-19 pandemic. The individual findings suggest that MAPs may enhance housing stability, improve health, safety, and well-being, reduce alcohol-related harms, and help participants improve their ability to follow COVID-19 guidelines. CONCLUSIONS: The COVID-19 pandemic accelerated the development of novel MAP models and approaches to alcohol distribution. The findings of this pilot evaluation illustrate the potential role for outreach models in the development of future MAPs.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".