Participation in Canadian Managed Alcohol Programs and Associated Probabilities of Emergency Room Presentation, Hospitalization and Death: A Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Managed Alcohol Programs (MAPs) are designed to improve health and housing outcomes for unstably housed people with an alcohol use disorder (AUD). The present study assesses the association of MAP participation with healthcare and mortality outcomes. METHODS: A retrospective cohort study assessed health outcomes for 205 MAP participants and 128 controls recruited from five Canadian cities in 2006-2017. Survival and negative binomial regression models were used to calculate hazard ratios (HR) of death and emergency room (ER) visits and hospital bed days (HBDs). Covariates included age, sex, AUD severity and housing stability score. RESULTS: In fully adjusted models, compared with times outside MAPs, participants had significantly reduced risk of mortality (HR = 0.37, P = 0.0001) and ER attendance (HR = 0.74, P = 0.0002), and fewer HBDs yearly (10.40 vs 20.08, P = 0.0184). Over the 12 years, people enrolled in a MAP at some point had significantly fewer HBDs per year than controls after MAP enrolment (12.78 vs 20.08, P = 0.0001) but not significantly different rates of death or ER presentation. MAP participants had significantly more alcohol-related but significantly fewer nonalcohol-related ER presentations than controls. CONCLUSION: Attendance at a MAP was associated with reduced risk of mortality or morbidity and less hospital utilization for individuals with unstable housing and severe AUDs. MAPs are a promising approach to reduce mortality risk and time spent in hospital for people with an AUD and experiencing homelessness.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".