2065. Increasing the HIV screening rates in Rural Syringes Service Programs
Bibliographic record
Abstract
Abstract Background The Kentucky Income Reinvestment Program (KIRP) is a statewide reinvestment of income generated by the Ryan White HIV/AIDS Program (RWHAP) Kentucky AIDS Drug Assistance Program, which provides comprehensive early intervention services targeting those at the highest risk for HIV infection. The objective of this study was to compare the HIV screening rates in persons who inject drugs (PWID) at Syringe Service Programs (SSP) supported by KIRP versus SSPs not supported by KIRP in Kentucky Methods A cross-sectional study was conducted on 54 SSPs. We compared the HIV screening rates in the first quarter of 2020, 2021, and 2022. Fisher's exact test compared the HIV screening rates between the KIRP supported SSPs (KSSP) versus Non-KIRP supported SSPs (NKSSP). Study data were collected and managed using REDCap electronic data capture tools hosted at Kentucky Department for Public Health. The statistical analyses were performed with Stata 17 (College Station, TX: Stata Corp LLC). Results The analysis included data of 21,072 PWID who attended the SSPs utilizing the REDCap electronic data capture tool during the study timeframe. In the first quarter of 2022, a total of 1,607/4795 (33.5%) PWID were screened for HIV at KSSP vs 12/482 (2.5%) at NKSSP (p=< 0.001). The difference in the screening rates has increased over the years since the KIRP began in 2019 (Figure 1). Conclusion The HIV screening rate in PWID has increased significantly in the SSPs supported by KIRP through RWHAP program income early intervention funds. This increase has occurred mainly in rural counties where people who inject drugs remain a significant population at risk for new HIV infections. Disclosures All Authors: No reported disclosures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".