Gender differences in the provision of injection initiation assistance: a comparison of three North American settings
Bibliographic record
Abstract
AIM: Individuals experience differential risks in their initiation into drug injecting based on their gender. Data suggest women are more likely to be injected after their initiator and to share injection equipment. Little is known, however, regarding how gender influences the risk that people who inject drugs (PWID) may assist others into injection initiation. We therefore sought to investigate the role of "initiator" gender in the provision of injection initiation assistance across multiple settings. METHODS: We employed data from PReventing Injecting by Modifying Existing Responses (PRIMER), a multi-cohort study investigating factors influencing injection initiation assistance provision. Data were drawn from three cohort studies of PWID in San Diego, USA (STAHR II); Tijuana, Mexico (El Cuete IV); and Vancouver, Canada (VDUS). Site-specific logistic regression models were fit, with lifetime provision of injection initiation assistance as the outcome and gender as the independent variable. RESULTS: Overall, 3.2% (24/746) of the women and 4.6% (63/1367) of the men reported providing injection initiation assistance. In Tijuana, men were more than twice as likely to have provided injection initiation assistance after controlling for potential confounders (adjusted odds ratio = 2.17, 95% confidence interval: 1.22-3.84). Gender was not significantly associated with providing injection initiation assistance in other sites. CONCLUSION: We identified that being male in Tijuana, specifically, was associated with providing injection initiation assistance, which could inform targeted outreach aimed at reducing the influence of PWID populations on non-injectors in this site. This will likely require that existing interventions address gender- and site-specific factors for effectiveness.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".