Involvement of people who inject drugs in injection initiation events: a cross-sectional analysis identifying similarities and differences across three North American settings
Bibliographic record
Abstract
Objectives People who inject drugs (PWID) play an integral role in facilitating the entry of others into injection drug use (IDU). We sought to assess factors influencing PWID in providing IDU initiation assistance across three distinct North American settings and to generate pooled measures of risk. Design We employed data from three PWID cohort studies participating in PReventing Injecting by Modifying Existing Responses (PRIMER), for this cross-sectional analysis. Setting Tijuana, Mexico; San Diego, USA; Vancouver, Canada. Participants A total of 2944 participants were included in this study (Tijuana: n=766, San Diego: n=353, Vancouver: n=1825). Measurements The outcome was defined as recently (ie, past 6 months) assisting in an IDU initiation event. Independent variables of interest were identified from previous PRIMER analyses. Site-specific multiple modified Poisson regressions were fit. Pooled relative risks (pRR) were calculated and heterogeneity across sites was assessed via linear random effects models. Results Evidence across all three sites indicated that having a history of providing IDU initiation assistance (pRR: 4.83, 95% CI: 3.49 to 6.66) and recently being stopped by law enforcement (pRR: 1.49, 95% CI: 1.07 to 2.07) were associated with a higher risk of providing assistance with IDU initiation; while recent opioid agonist treatment (OAT) enrolment (pRR: 0.64, 95% CI: 0.43 to 0.96) and no recent IDU (pRR: 0.21, 95% CI: 0.07 to 0.64) were associated with a lower risk. We identified substantial differences across site in the association of age (I 2 : 52%), recent housing insecurity (I 2 : 39%) and recent non-injection heroin use (I 2 : 78%). Conclusion We identified common and site-specific factors related to PWID’s risk of assisting in IDU initiation events. Individuals reporting a history of assisting IDU initiations, being recently stopped by law enforcement, and recently injecting methamphetamine/speedball were more likely to have recently assisted an IDU initiation. Whereas those who reported not recently engaging in IDU and those recently enrolled in OAT were less likely to have done so. Interventions and harm reduction strategies aimed at reducing the harms of IDU should incorporate context-specific approaches to reduce the initiation of IDU.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".