Nurse-led safer opioid supply and HIV pre-exposure prophylaxis: a novel pilot project
Bibliographic record
Abstract
Introduction: HIV pre-exposure prophylaxis (PrEP) is an effective intervention for preventing HIV infections yet is largely unknown to and underutilized among people who use drugs. Methods: To better provide services to this group, we present a prospective, single-group interventional study involving the creation of a partnership between a safer opioid supply program and an HIV PrEP program, both of which were nurse-led. Results: = 23) acceptance. Almost half of the group that accepted PrEP identified as female, and nearly all participants were homeless and did not have a primary care provider. While it was challenging to obtain routine PrEP follow-up labs per guideline recommendations due to poor venous access, most participants were able to successfully stay on PrEP and maintained good medication adherence. There were no PrEP discontinuations due to renal impairment and no participants tested positive for HIV. Conclusion: This novel integration of programs appeared to be a highly effective way to expand access to HIV prevention among people who use drugs. Given the historical and current mistreatment of people who use drugs within the healthcare system, rapport and trust were essential to the uptake of HIV PrEP services. Further, the importance of infectious disease screening among people who use drugs is underscored, and built-in program flexibility and low barrier access is essential.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".