The Community-based Responses to the Opioid Crisis: Lessons Learned from Vancouver, Canada
Bibliographic record
Abstract
Overdose deaths and non-fatal overdoses have increased in recent years across North America, due to the increased potency of fentanyl and its analogues, and the addition of other substances, such as benzodiazepines. Facing with the challenges of the opioid crisis, many North American cities have responded differently. These responses mainly focus on one field, such as medical care, law enforcement, judicature, and social security. Among those cities in North America, the community-based crisis response model in Vancouver is worthy of reference by other cities. According to the ethnographic fieldwork conducted in Vancouver Downtown Eastside in 2019, this paper argues that Vancouver has gradually formed a community-based response model since the establishment of Insite, which is the first supervised injection site in North America. The specific performance is as follows: Crosstown Clinic providing hydromorphone for drug addicts, OPS rescuing the overdose people, and Safe Supply strategy. In addition, according to relevant literature review, we found that responding to the challenges of the COVID-19, Vancouver also made some rewarding attempts to deal with the opioid crisis, such as compassion clubs and capsule fentanyl, all of which are the actions to drive the Safe Supply. And the decriminalization of possession certain amount hard drugs from January 31, 2023 in B.C.is another exemption approved by Canadian Government. In brief, Vancouver's community-based crisis response model promotes Vancouver to collaborate in the fields of activist, medical care, law enforcement, judicature, and social security to jointly participate in the governance of the opioid crisis. This paper argues that the lessons learned from Vancouver are the living representation of community empowerment which is confident, resilient, independent and energetic, which has the capacity to identify problems and design solutions at the local level, and which is inclusive and voluntary.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".