Giving away used injection equipment: missed prevention message?
Bibliographic record
Abstract
BACKGROUND: Our objective was to examine factors associated with distributive injection equipment sharing and how needle exchange programs (NEPs) can help reduce distributive sharing among injection drug users (IDUs). METHODS: 145 English speaking Canadian IDUs ages 16 years and over who had injected in the past 30 days were recruited for a cross-sectional survey. Participants were asked about their socio-demographic characteristics, HIV risk behaviours, social support, drug treatment readiness, program satisfaction, health and social service use and NEP drug use. Bivariate statistics and logistic regression were used to characterize the population and examine correlates of sharing behaviour. RESULTS: More IDUs reported distributive sharing of cookers (45%) than needles (36%) or other types of equipment (water 36%; filters 29%; swabs 8%). Regression analyses revealed the following factors associated with distributing used cookers: a history of cocaine/crack injection, an Addiction Severity Index (ASI) score indicative of a mental health problem, and older than 30 years of age. Factors associated with giving away used water included: male, injected methadone, injected other stimulants and moved 3+ times in the past 6 months. Factors associated with giving away used filters included: injected cocaine/crack or stayed overnight on the street or other public place. Factors associated with giving away swabs included: an ASI mental health score indicative of a mental health problem, and HCV negative status. CONCLUSIONS: Our findings show that more IDUs give away cookers than needles or other injection equipment. While the results showed that correlates of sharing differed by piece of equipment, each point to distributive sharing by the most marginalized IDUs. Targeting prevention efforts to reduce equipment sharing in general, and cookers in particular is warranted to reduce use of contaminated equipment and viral transmission.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".