A Comparison of Perinatal HIV Prevention Opportunities for Hispanic and Non-Hispanic Women in California
Bibliographic record
Abstract
Using a semi-structured survey and convenience sample of pregnant/recently delivered Hispanic (n = 453) and non-Hispanic (n = 904) women in four California counties, this study compared rates of timely prenatal care (PNC) initiation, HIV test counseling, test offering, and test acceptance in PNC between Hispanic and non-Hispanic women. Hispanic women were less likely to report timely PNC initiation (69.3% vs. 80.4%, p < .0001), receiving test offer (69.5% vs. 76.7%, p = .002), and ever having been tested (77.3% vs. 87.9%, p < .0001) than non-Hispanic women. Hispanic women were more likely to report not knowing where to go (p = .04) and having no insurance (p < .001), transportation (p = .001), and child care (p = .007) as reasons for late PNC start. Both Hispanic and non-Hispanic women most commonly accepted a test offer for their health/health of their baby; Hispanic women were more likely to accept based on doctor/nurse recommendation (80.1% vs. 62.7%, p < .001). A quarter of Hispanic and non-Hispanic women reported they didn't feel they had a choice or that test was done automatically. Efforts to improve perinatal HIV prevention opportunities for all women in California are required. Furthermore, Hispanic women may have disparities in receipt of prenatal care and HIV test offer that need additional attention.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".