Examining the Effect of a Hypothetical Safe Injection Facility on HIV and HCV Transmission Rates in Kent County, Michigan
Bibliographic record
Abstract
Previous literature has established that increases in prescription opioid misuse has resulted in similar increases in injection drug use (IDU), collectively referred to as the “opioid epidemic” in the US. Due to this surge in IDU, incidence of Hepatitis C (HCV) and Human Immunodeficiency Virus (HIV) are on the rise in many regions. Research conducted in Canada and elsewhere has supported the use of Safe Injection Facilities (SIFs) and Needle Syringe Programs (NSPs) to mollify disease incidence, but only NSPs are operating in the US despite SIFs having been in use in Canada for several decades. As little research has been conducted in the US about where SIFs could be of benefit, we employ an analytical model to estimate the value of a hypothetical SIF in Kent County, Michigan (MI) using local surveillance data. Addition of such a facility was found to reduce HCV incidence by 7 cases / year, in addition to decreasing HIV incidence by 5%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".