Requiring help injecting among people who inject drugs in Toronto, Canada: Characterising the need to address sociodemographic disparities and substance‐use specific patterns
Bibliographic record
Abstract
INTRODUCTION: Those requiring help injecting are at an elevated risk of injection-related injury and blood-borne infections and are thus a priority group for harm reduction programs. As supervised consumption services (SCS) are scaled-up across Canada, information on those who require help injecting is necessary to inform equitable service uptake. We characterised the sociodemographic, structural and drug use correlates of needing help injecting among a cohort of people who inject drugs in Toronto, Canada. METHODS: A cross-sectional baseline survey was administered between November 2018 and March 2020. Unadjusted and multivariable logistic regression models examined associations with requiring help injecting in the past 6 months. A gender-stratified sub-analysis described characteristics of receiving help among those requiring it. RESULTS: Of 701 participants (31.0% cisgender women), 294 (41.9%) needed recent help injecting. In unadjusted analyses, being a racialised, non-Indigenous person (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.13-2.86) or a cisgender woman (OR 1.72, 95% CI 1.24-2.39) were associated with needing help. In multivariable analyses, requiring assistance was associated with needing frequent help preparing drugs (adjusted OR [AOR] 9.52, 95% CI 4.78-21.28), fewer years since first injection (AOR for 1 year increase: 0.97, 95% CI 0.95-0.99) and injecting stimulants. Among those who required help, cisgender women reported needing assistance more often than cisgender men (P = 0.009). DISCUSSION AND CONCLUSIONS: Over two-fifths of the sample required help injecting; requiring assistance was associated with sociodemographic indicators and substance use-specific patterns. Findings highlight the need to scale-up educational resources for those who receive or provide help injecting, as well as SCS that accommodate onsite injection assistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".