A qualitative study on perceptions and experiences of overdose among people who smoke drugs in Vancouver, British Columbia
Bibliographic record
Abstract
BACKGROUND: Smoking unregulated drugs has increased substantially in British Columbia. Intersecting with the ongoing overdose crisis, drug smoking-related overdose fatalities have correspondingly surged. However, little is known about the experiences of overdose among people who smoke drugs accessing the toxic drug supply. This study explores perceptions and experiences of overdose among people who smoke drugs. METHODS: We conducted interviews with 31 people who smoke drugs. Interviews covered a range of topics including overdose experience. Thematic analysis was used to identify themes related to participant perceptions and experiences of smoking-related overdose. RESULTS: Some participants perceived smoking drugs to pose lower overdose risk relative to injecting drugs. Participants reported smoking-related overdose experiences, including from underestimating the potency of drugs, the cross-contamination of stimulants with opioids, and responding to smoking-related overdose events. CONCLUSIONS: Findings highlight the impact the unpredictable, unregulated, and toxic drug supply is having on people who smoke drugs, both among people who use opioids, and among those who primarily use stimulants. Efforts to address smoking-related overdose could benefit from expanding supervised smoking sites, working with people who use drugs to disseminate accurate knowledge around smoking-related overdose risk, and offering a smokable alternative to the unpredictable drug supply.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".