Experiences of stigmatization among people who use drugs in the initial year of British Columbia's drug decriminalization policy: A qualitative study
Bibliographic record
Abstract
BACKGROUND: On January 31, 2023, Health Canada approved a three-year pilot decriminalization initiative allowing adults in British Columbia (BC), Canada, to legally possess a cumulative 2.5 g of opioids, cocaine, methamphetamine, or MDMA. The policy aims to reduce the harms associated with drug use, including stigma, which contributes to barriers for people who use drugs in accessing harm reduction and treatment services. Addressing stigma is considered a crucial step in mitigating BC's overdose crisis, by encouraging people who use drugs to feel more comfortable to access and utilize services without fear of judgment. This study examined the impacts of the decriminalization policy on stigma among people who use drugs within the first year of implementation. METHODS: From October 2023 to February 2024, 100 semi-structured telephone interviews and brief socio-demographic surveys with people who use drugs from across BC were conducted to explore their experiences of stigmatization post-decriminalization. A qualitative thematic analysis was used to synthesize the data. RESULTS: Findings revealed that societal, structural and self-stigma remain pervasive among people who use drugs post-decriminalization. While most participants reported little change in their experiences of stigma since the policy's implementation, some reported an increase, while others observed a decrease across all forms of stigma. Increased stigma was attributed to perceived heightened visibility of public drug use, which amplified societal stigma. Conversely, decreased stigma was linked to shifting public perceptions of drug use as a public health issue and more humanized interactions with the police. Despite mixed experiences, participants remained optimistic that the policy could contribute to long-term stigma reduction and offered recommendations to support this goal. CONCLUSION: While decriminalization has the potential to reduce stigma and increase comfort in accessing critical harm reduction and substance use treatment services among people who use drugs, achieving these outcomes requires additional efforts. Public awareness campaigns, targeted anti-stigma education, and broader systemic changes were suggested by participants as essential to address stigmatization in BC. Strengthening these areas could enhance access to and utilization of services, ultimately supporting the broader goals of decriminalization.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".