How do people with homelessness and alcohol dependence cope when alcohol is unaffordable? A comparison of residents of Canadian managed alcohol programs and locally recruited controls
Bibliographic record
Abstract
INTRODUCTION AND AIMS: We investigated coping strategies used by alcohol-dependent and unstably housed people when they could not afford alcohol, and how managed alcohol program (MAP) participation influenced these. The aim of this study was to investigate potential negative unintended consequences of alcohol being unaffordable. DESIGN AND METHODS: A total of 175 MAP residents in five Canadian cities and 189 control participants from nearby shelters were interviewed about the frequency they used 10 coping strategies when unable to afford alcohol. Length of stay in a MAP was examined as a predictor of negative coping while controlling for age, sex, ethnicity, housing stability, spending money and drinks per day. Multivariate binary logistic and linear regression models were used. RESULTS: Most commonly reported strategies were re-budgeting (53%), waiting for money (49%) or going without alcohol (48%). A significant proportion used illicit drugs (41%) and/or drank non-beverage alcohol (41%). Stealing alcohol or property was less common. Long-term MAP participants (>2 months) exhibited lower negative coping scores than controls (8.76 vs. 10.63, P < 0.001) and were less likely to use illicit drugs [odds ratio (OR) 0.50, P = 0.02], steal from liquor stores (OR 0.50, P = 0.04), re-budget (OR 0.36, P < 0.001) or steal property (OR 0.40, P = 0.07). Long-term MAP participants were also more likely to seek treatment (OR 1.91, P = 0.03) and less likely to go without alcohol (OR 0.47, P = 0.01). DISCUSSION AND CONCLUSIONS: People experiencing alcohol dependence and housing instability more often reduced their alcohol consumption than used harmful coping when alcohol was unaffordable. MAP participation was associated with fewer potentially harmful coping strategies.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".