Does managing the consumption of people with severe alcohol dependence reduce harm? A comparison of participants in six Canadian managed alcohol programs with locally recruited controls
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Managed alcohol programs (MAP) are intended for people with severe alcohol-related problems and unstable housing. We investigated whether MAP participation was associated with changes in drinking patterns and related harms. DESIGN AND METHODS: One hundred and seventy-five MAP participants from five Canadian cities (Hamilton, Ottawa, Toronto, Thunder Bay and Vancouver) and 189 same-city controls were assessed for alcohol consumption, health, safety and harm outcomes. Length of stay in a MAP was investigated as a predictor of drinking patterns, non-beverage alcohol consumption and related harms. Statistical controls were included for housing stability, age, gender, ethnic background and city of residence. Negative binomial regression and logistic regression models were used. RESULTS: Recently admitted MAP participants (≤2 months) and controls were both high consumers of alcohol, predominantly male, of similar ethnic background, similarly represented across the five cities and equally alcohol dependent (mean Severity of Alcohol Dependence Questionnaire = 29.7 and 31.4). After controlling for ethnicity, age, sex, city and housing stability, long-term MAP residents (>2 months) drank significantly more days (+5.5) but 7.1 standard drinks fewer per drinking day than did controls over the last 30 days. Long-term MAP residents reported significantly fewer alcohol-related harms in the domains of health, safety, social, legal and withdrawal. DISCUSSION AND CONCLUSIONS: Participation in a MAP was associated with more frequent drinking at lower quantities per day. Participation was associated with reduced alcohol-related harms over the past 30 days. Future analyses will examine outcomes longitudinally through follow-up interviews, police and health care records.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".