“We’ve lost a lot of lives:” the impact of the closure of North America’s busiest supervised consumption site on people who use substances and the organizations that work with them
Bibliographic record
Abstract
BACKGROUND: Supervised Consumption Sites (SCS) are an evidence-based harm reduction intervention that reduces the risk of fatal drug poisonings. However, these approaches have faced political opposition in Canada, resulting in the closures of SCS in some provinces. Our study examines the aftermath of the closure of what was once North America's busiest SCS, located in Lethbridge, Alberta, Canada, offering a contextualized exploration of regressive drug policies. METHODS: Our study adopts a descriptive qualitative design to explore the Lethbridge SCS closure and the city's current state of harm reduction service provision. We conducted 37 interviews to understand the perspectives of people who use substances (PWUS) and staff members of organizations that provide harm-reduction services in Lethbridge. We chose to use reflexive thematic analysis, which allows for a critical realist and contextual approach to data analysis. RESULTS: We developed three themes based on our analysis. Our first theme speaks to the harms of SCS closures on PWUS and organizations that provide harm reduction services. Next, our second theme highlights participants' perspectives on the political motivations behind the SCS closure. Our last theme explores how PWUS and organizations navigate the political opposition to harm reduction approaches while responding to the worsening unregulated drug poisoning crisis. CONCLUSIONS: Our findings speak to the dangers of political decisions that restrict access to harm reduction services within the context of the current unregulated drug poisoning crisis.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".