Young, Black/African American, and Latino communities are left behind despite legislative efforts in California to reduce HIV/STI disparities
Bibliographic record
Abstract
Objectives: Sexually transmitted infections (STI) have been on the rise in the United States with racial/ethnic minority groups, gay and bisexual men, and youth experiencing the highest STI and HIV infection rates. In 2022, California became the first state in the nation to pass legislation, Senate Bill 306 (SB 306), requiring health care plans to cover the costs of home test kits for STIs, including HIV. This study examines provisions within SB 306 and its potential to reduce STI and HIV disparities among key demographic groups and geographic regions within California. Study design: Ecological cross-sectional study involving 58 California counties. Methods: Descriptive statistics and choropleth maps compared HIV/STI prevalence rates, uninsured rates, demographic composition, and healthcare provider coverage across California counties. Three geographically weighted Poisson regression analyses were conducted to separately examine the association between proportion of uninsured and HIV, gonorrhea, and chlamydia prevalence rates. Results: HIV/STI rates were significantly and positively associated with the proportion of uninsured residents in Central and Southern California counties. These counties had a higher proportion of demographic groups vulnerable to HIV/STI including a large Latino, Black/African American, and younger (age 15-24) population but had a lower rate of healthcare providers with prescription authority for home testing kits, which is a requirement under SB 306. Conclusions: Cutting-edge solutions are needed to stem the rising tide of new STI and HIV infections. While SB 306 is novel and innovative in intent, its coverage gaps will increase disparities and inequities among historically underserved populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".