Exploring the social and risk networks of male and female injection drug users in Toronto
Bibliographic record
Abstract
Background. Injection drug users (IDUs) are at increased risk for contracting bloodborne infections. Individually focussed interventions have led to risk reductions but do not recognize that risk occurs within relationships. Network based interventions may add to harm reduction strategies that prevent the transmission of blood borne infections. To be able to develop effective interventions requires knowledge about IDUs' networks and the impact of networks on behaviour. Conclusions. While network-based prevention strategies may provide an additional level of harm reduction for injection drug users, programs should consider the differential impact of networks on male and female injection drug users and take these differences into consideration when designing effective strategies. Methods. A convenience sample of 150 IDU (75 males and 75 females) from the city of Toronto was interviewed in 2004. Participants were recruited through a number of sources and from across the city in an effort to include a diverse cross section. Respondents were asked a series of questions about themselves, their drug use and their risk behaviours. Drug, sex and support networks were elicited and questions about each contact were asked. Analyses at the participant level were logistic regression models that adjusted for confounding variables. Analysis at the level of the dyad involved hierarchical models that adjusted for data dependencies using generalized estimating equations with repeated measures corrections. Analyses were gender-stratified. Results. The analysis of network characteristics showed significant associations with risk participation, however male and female IDUs were not affected in the same way by their networks. While female IDUs rates of reporting participation in risk behaviours were affected by the inclusion of supportive, close drug relationships, male injectors seem to be most affected by the number of drug contacts that they had, their participation in the drug economy and their own levels of drug use. This suggests that women who inject drugs may be more readily influenced by their networks than male injectors. Objective. This thesis will describe the gender differences in the associations between egocentric network characteristics (size, multiplexity (relationship overlap), 'closeness') and injection risk behaviours (receptive needle sharing, sharing of injection paraphernalia and syringe mediated sharing).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".