Perceived Everyday Racism, Residential Segregation, and HIV Testing Among Patients at a Sexually Transmitted Disease Clinic
Bibliographic record
Abstract
OBJECTIVES: More than one quarter of HIV-infected people are undiagnosed and therefore unaware of their HIV-positive status. Blacks are disproportionately infected. Although perceived racism influences their attitudes toward HIV prevention, how racism influences their behaviors is unknown. We sought to determine whether perceiving everyday racism and racial segregation influence Black HIV testing behavior. METHODS: This was a clinic-based, multilevel study in a North Carolina city. Eligibility was limited to Blacks (N = 373) seeking sexually transmitted disease diagnosis or screening. We collected survey data, block group characteristics, and lab-confirmed HIV testing behavior. We estimated associations using logistic regression with generalized estimating equations. RESULTS: More than 90% of the sample perceived racism, which was associated with higher odds of HIV testing (odds ratio = 1.64; 95% confidence interval = 1.07, 2.52), after control for residential segregation, and other covariates. Neither patient satisfaction nor mechanisms for coping with stress explained the association. CONCLUSIONS: Perceiving everyday racism is not inherently detrimental. Perceived racism may improve odds of early detection of HIV infection in this high-risk population. How segregation influences HIV testing behavior warrants further research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".