Using social networks to better conceptualize risk for bloodborne viruses among injection drug users
Bibliographic record
Abstract
Introduction. Injection drug users (IDUs) are at high risk for infection with HIV, hepatitis C virus (HCV), and other bloodborne viruses through the sharing of drug injection equipment. It is becoming widely recognized that the transmission of infection in this population occurs as a result of individual risk behaviours within the context of social networks. Objectives. The goal of this thesis was to identify salient features of social and drug injecting networks of IDUs that potentially facilitate infection transmission. The main objectives were: (1) to examine whether risk factors for infection depend on the characteristics of the drug network to which an IDU belongs; (2) to investigate whether risk behaviours exist between injecting partners who participate in the harm reduction practice of sterile equipment exchange; and (3) to explore the association between concordance of bloodborne virus infection status of injecting partners and drug equipment sharing. Methods. A cross-sectional study recruited active IDUs from syringe exchange and methadone treatment programs in Montreal, Canada, during 2004-2005. Results. The extent of risk factors for bloodborne infections can be differentiated by the type of network to which an IDU belongs, whereby cocaine using networks have a higher probability of risk factors than heroin networks. Despite the health benefits offered by the exchange of sterile syringes between IDUs, injecting partners remain at risk for infection through concomitant risk behaviours. Finally, perceived risk of infection appears to play a minor role in reducing injection risk behaviours since drug equipment continues to be shared between injecting partners who are discordant for HIV or HCV infection status. Conclusion. Social networks add a dimension of risk beyond an IDU's personal injection practices. Public health interventions should emphasize network-related risk factors at both the whole network and partnership levels in order to reduce injection behaviours associated with the transmission of bloodborne viruses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".