Non-beverage alcohol consumption among individuals experiencing chronic homelessness in Edmonton, Canada: a cross-sectional study
Bibliographic record
Abstract
Background Among individuals experiencing homelessness, the prevalence of alcohol use disorder is extremely high. Alcohol-related harms are compounded by the use of non-beverage alcohol (NBA; e.g. rubbing alcohol, cooking wine). The dangers of NBA consumption pose significant risks to the individual and to others when consumed in large quantities and when mixed with other substances. The objectives of this paper are to describe the alcohol consumption patterns of individuals experiencing homelessness, identify substance use patterns, psychological stressors, and related harms associated with NBA consumption, and compare NBA consumers to non-NBA consumers in relation to their use of services and perceived barriers to care. Methods Using a cross-sectional survey, 150 individuals experiencing homelessness were recruited from Edmonton’s inner city and adjoining areas. Frequency, quantity, and volume of alcohol consumption were used to assess patterns of alcohol use in the last 6 months. Descriptive statistics and bivariate analyses were used to compare participants reporting NBA consumption and non-NBA consumption (p ≤ 0.05). Results The majority of participants were male (71.3%) and self-identified as Indigenous (74.0%). Overall, 24% (n = 36) reported NBA consumption within the last six months. NBA consumers were older than non-NBA consumers (p = 0.005), reported different perceived living stability (p = 0.022), and had higher psychological distress (p = 0.038). The majority of NBA consumers reported not receiving harm reduction services while also not needing such services (n = 18, 51.4%), which differed from non-NBA consumers (p = 0.003). Structural barriers (e.g. availability, location, cost) were most frequently reported as reasons for unmet harm reduction (60.9%) and hospital care (58.3%) needs, while barriers to skills training (58.5%) and counselling services (53.6%) were mostly motivational (e.g. personal beliefs). Conclusions Within such an already marginalized population experiencing homelessness, individuals who consume NBA represent a vulnerable subpopulation who require adapted and distinct health and social services to stabilize and recover. Current harm reduction services are not prepared to effectively assist this group of individuals, and specific treatment programs are rare. Managed alcohol programs are a feasible approach but must be tailored to the specific needs of those who consume NBA, which is especially important for Indigenous people. More comprehensive assessments of NBA consumption are needed for program development and policy recommendations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".