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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".