Barriers to HIV-Testing Among Hispanics in the United States: Analysis of the National Health Interview Survey, 2000
Bibliographic record
Abstract
Data from the 2000 National Health Interview Survey (NHIS) were analyzed to explore barriers to HIV testing, and intentions to be tested among a nationally representative sample (n = 4,261) of the different Hispanic subgroups living in the United States. Weighted proportions and variances accounting for the complex sample design of the NHIS were estimated using the Taylor series linearization method. Regression estimates are expressed as odds ratios and their 95% confidence intervals. Two thirds of sampled Hispanics had never been tested for HIV (excluding blood donations) and 88% expressed no intention to do so in the near future. Many of the factors that influence the likelihood of having been tested in the past also impact on future HIV testing intentions including age, Hispanic subgroup, high-risk status, and self-perceived HIV risk. Compared to Puerto Ricans, Mexicans (odds ratio [OR] = 1.59, 1.1-2.3) and Mexican/Americans (OR = 1.61, 1.1-2.3) were more likely to never have been tested and Cubans were notably more likely to report negative future testing intentions (OR = 5.63, 2.5-12.8). Among Hispanics who reported high-risk status or high/medium self-perceived HIV risk, more than one quarter had never undergone testing and expressed no intention of doing so in the near future. Recognition of the HIV testing barriers identified in this study is valuable for the development and refinement of current strategies that aim to increase HIV testing practices in the heterogeneous U.S. Hispanic population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".