Community Programming in Mental Health Care Planning: A Case Study at the Drinkers Lounge in Vancouver, BC
Bibliographic record
Abstract
The Drinkers Lounge is an innovative harm reduction drop-in centre for drinkers in the Downtown Eastside (DTES) of Vancouver. Drinkers in this community are arguably the most street-entrenched population in the DTES because they are barred from almost every public space in Vancouver (Maynard 2019). Many of the drinkers are Indigenous, which means they experience racism in addition to the discrimination and the stigma that is associated with living in poverty and drinking. Most services for drinkers and other substance users are informed by biomedical and neoliberal ideology, which pathologizes individuals and commonly takes an abstinence approach to care. The Drinkers Lounge focuses instead on the social determinants that lead to substance use, such as a history of personal trauma, ongoing discrimination, and colonial and neoliberal policy. Rather than focusing on abstinence, they offer a range of supports to the drinkers to improve their health and well-being in many aspects of their lives. For the Drinkers Lounge to connect this population to these supports and services, they have had to create an innovative and radical space that is welcoming to the most marginalized members of the community. They have done this by embodying three main principles: (1) a focus on meaningful community building, (2) valuing the lived expertise of the community members, and (3) considering Indigenous approaches to care. This model has many perceived benefits and is widely credited as lifesaving by community members. Nevertheless, the Drinkers Lounge continues to struggle for survival and sustainable funding.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".