Responding to an Explosive HIV Epidemic Driven by Frequent Cocaine Injection: Is There a Role for Safe Injecting Facilities?
Bibliographic record
Abstract
Although there have been repeated calls for the establishment of safe injection facilities (SIFs) in Vancouver since the early 1990s, questions remain concerning the feasibility of SIFs due to the high prevalence of injection cocaine and the concomitant problems cocaine use presents. Therefore, we determined the prevalence of willingness to attend SIFs among cocaine injectors in Vancouver and explored the factors associated with this willingness, using data from the Vancouver Injection Drug Users Study. After considering the results derived from this analysis, additional qualitative methods were employed to explore further willingness to use SIFs, barriers and facilitators of SIF utilization, and methods of maximizing use among selected subpopulations of cocaine injectors. The results suggest that a high proportion of cocaine injectors, including some of those most at risk, would attend an SIF if one were available. However, in order to better accommodate cocaine injectors, several modifications could be made to conventional SIF service design and delivery. The vast majority of these modifications relate to ensuring effective responses to cocaine toxicity. Given the acceptability of SIFs among cocaine injectors, it appears that an SIF pilot could result in significant and immediate benefits in terms of public health and community safety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".