Injecting drugs alone during an overdose crisis in Vancouver, Canada
Bibliographic record
Abstract
Background: Settings throughout Canada and the USA continue to experience crises of overdose death due to the toxic unregulated drug supply. Injecting drugs alone limits the potential for intervention and has accounted for a significant proportion of overdose deaths, yet the practice remains understudied. We sought to examine the practice of injecting alone among people who inject drugs (PWID) in Vancouver, Canada. Methods Data were derived from two prospective cohorts of people who use drugs between June 2016 and November 2018. This analysis was restricted to participants who, in the previous 6 months, reported any injection drug use. Rates of injecting alone were categorized as always, usually, sometimes, or occasionally. We fit a multivariable generalized linear mixed model to identify factors associated with injecting drugs alone. Results Among 1070 PWID who contributed 3307 observations, 931 (87%) reported injecting alone at least once during the study period. In total, there were 729 (22%) reports of always injecting alone, 722 (21.8%) usually, 471 (14.2%) sometimes, 513 (15.5%) occasionally, and 872 (26.4%) never. In a multivariable model, factors positively associated with injecting drugs alone included male sex (adjusted odds ratio [AOR] 1.69; 95% confidence interval [CI] 1.20–2.37), residence in the Downtown Eastside neighbourhood (AOR 1.43; 95% CI 1.08–1.91), binge drug use (AOR 1.36; 95% CI 1.08–1.72), and experiencing physical or sexual violence or both (AOR 1.43; 95% CI 1.00–2.03). Protective factors included Indigenous ancestry (AOR 0.71; 95% CI 0.52–0.98) and being in a relationship (AOR 0.30; 95% CI 0.23–0.39). Conclusion We observed that injecting alone, a key risk for overdose mortality, was common among PWID in Vancouver. Our findings underline the need for additional overdose prevention measures that are gender-specific, culturally appropriate, violence- and trauma-informed, and available to those who inject alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".