Vancouver’s Alcohol Knowledge Exchange: lessons learned from creating a peer-involved alcohol harm reduction strategy in Vancouver’s Downtown Eastside
Bibliographic record
Abstract
Despite high rates of harm attributable to alcohol use itself and the associated marginalization of illicit drinkers in Vancouver's Downtown Eastside (DTES), alcohol-specific harm reduction services there are under-resourced and highly disconnected from one another. In response to these conditions and high rates of death amongst its membership, the Eastside Illicit Drinkers Group for Education, an affiliate group of the Vancouver Area Network of Drug Users, convened a regular meeting of stakeholders, termed a "community of practice" in 2019 to bring together peers who used beverage and non-beverage alcohol, shelter and harm reduction service providers, public health professionals, clinicians, and policymakers to improve system-level capacity to reduce alcohol-related harm. The discussions that followed from these meetings were transformed into the Vancouver Alcohol Strategy (VAS), a comprehensive, harm reduction-oriented policy framework for alcohol harm reduction in the DTES. This article highlights our experiences producing community-led alcohol policy through the VAS with specific attention to the ways in which people who use alcohol themselves were centred throughout the policy development process. We also provide summary overviews of each of the VAS document's 6 thematic areas for action, highlighting a sampling of the 47 total unique recommendations. Historically, people who use non-beverage alcohol and whose use of alcohol in public spaces is criminalized due to housing precarity and visible poverty have been excluded from the development of population-level alcohol policies that can harm this specific population. The process of policy development undertaken by the VAS has attempted to resist this top-down approach to public health policy development related to alcohol control by intentionally creating space for people with lived experience to guide our recommendations. We conclude by suggesting that a grassroots enthusiasm for harm reduction focused policy development exists in Vancouver's DTES, and requires resources from governmental public health institutions to meaningfully prevent and reduce alcohol-related and policy-induced harms.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".