Perceived harms and harm reduction strategies among people who drink non-beverage alcohol: Community-based qualitative research in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: There has been increasing interest in harm reduction initiatives for street-involved people who drink alcohol, including non-beverage alcohol such as mouthwash and hand sanitizer. Limited evidence exists to guide these initiatives, and a particular gap is in research that prioritizes the experiences and perspectives of drinkers themselves. This research was conducted to explore the harms of what participants termed "illicit drinking" as perceived by people who engage in it, to characterize the steps this population takes to reduce harms, and to identify additional interventions that may be of benefit. METHODS: This participatory qualitative research drew on ethnographic approaches including a series of 14″town hall"-style meetings facilitatied and attended by people who self identify as drinking illicit or non-beverage alcohol (n = 60) in Vancouver, British Columbia. This fieldwork was supplemented with four focus groups to explore emerging issues. RESULTS: Participants in the meetings described the harms they experienced as including unintentional injury; harms to physical health; withdrawal; violence, theft, and being taken advantage of; harms to mental health; reduced access to services; and interactions with police. Current harm reduction strategies involved balancing the risks and benefits of drinking in groups and adopting techniques to avoid withdrawal. Proposed future initiatives included non-residential managed alcohol programs and peer-based supports. CONCLUSIONS: Illicit drinkers describe harms and harm reductions strategies that have much in common with those of other illicit substances, and can be interpreted as examples of and responses to structural and everyday violence. Understanding the perceived harms of alcohol use by socially marginalized drinkers and their ideas about harm reduction will help tailor programs to meet their needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".