Awareness and knowledge of drug decriminalization among people who use drugs in British Columbia: a multi-method pre-implementation study
Bibliographic record
Abstract
BACKGROUND: In January 2023, British Columbia implemented a three-year exemption to Controlled Drugs and Substances Act, as granted by the federal government of Canada, to decriminalize the personal possession of small amounts of certain illegal drugs. This decriminalization policy, the first in Canada, was announced in response to the overdose emergency in British Columbia as a public health intervention that could help curb overdose deaths by reducing the impact of criminalization and increasing access to health and social services through stigma reduction. METHODS: The current multi-method study examines people who use drugs' awareness and knowledge of British Columbia's decriminalization model through cross-sectional quantitative surveys and qualitative interviews among people who use drugs from September-November 2022, immediately prior to the implementation of decriminalization. RESULTS: Quantitative findings show that two-thirds (63%) of people who use drugs were aware of the policy, but substantial knowledge gaps existed about the legal protections afforded (threshold amount, substances included, drug trafficking, confiscation). The qualitative findings suggest that people who use drugs misunderstood the details of the provincial decriminalization model and often conflated it with regulation. Results suggest that information sharing about decriminalization were minimal pre-implementation, highlighting areas for knowledge dissemination about people who use drugs' rights under this policy. CONCLUSIONS: Given that decriminalization in British Columbia is a new and landmark reform, and that the success of decriminalization and its benefits may be undermined by poor awareness and knowledge of it, efforts to share information, increase understanding, and empower the community, may be required to promote its implementation and benefits for the community.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".