Exploring the early impacts of drug decriminalization on harm reduction and opioid agonist treatment service operations and delivery in British Columbia: insights from key informant interviews
Bibliographic record
Abstract
BACKGROUND: On January 31, 2023, Health Canada granted British Columbia (BC) a three-year (2023-2026) exemption under the Controlled Drugs and Substances Act (CDSA), decriminalizing the personal possession of up to 2.5 g of certain unregulated drugs among adults (18+) without arrest, seizure of drugs, or criminal penalty. A key objective was to increase awareness, engagement, and retention in harm reduction (HR) and opioid agonist treatment (OAT) services by reducing stigma and enhancing service access. In May 2024, however, the policy was amended to re-criminalize drug use and possession in public spaces. This study examines how decriminalization and its subsequent amendment affected HR and OAT service operations from the perspective of service providers across BC. METHODS: Between October 23rd, 2024 and January 29th, 2025, 18 semi-structured virtual key informant interviews were conducted with HR and OAT service providers across BC. The interviews examined participants' experiences with decriminalization and its amendment, and how these policy changes impacted service operations and delivery. Thematic analysis was used to identify patterns across interview data. RESULTS: Key informants reported few operational changes following decriminalization, with no major adjustments to service delivery. Despite expectations of increased client engagement, sites received no additional funding and faced ongoing staffing and resource shortages. Informants emphasized that systemic issues-such as the toxic drug supply and rising homelessness-had a greater impact on service use than the policy itself. A lack of clear communication and site-specific training resulted in uncertainty and confusion, further limiting frontline staff's ability to respond effectively to policy changes. CONCLUSION: Key informants perceived that decriminalization did not lead to immediate changes in HR and OAT site operations. Longstanding systemic barriers continued to limit service capacity and policy impact. Providers highlighted the need for sustained investment in housing, staffing, and supervised spaces to support meaningful engagement and reduce stigma. Addressing these foundational issues was seen as essential for realizing the policy's intended public health objectives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".