“At Risk” Women Who Think That They Have No Chance of Getting HIV: Self-Assessed Perceived Risks
Bibliographic record
Abstract
During the past two decades, a fair amount of inconclusive research has been conducted to examine the relationship between perceived risk of contracting HIV and actual HIV risk behavior practices. The present study examines HIV risk perceptions among a sample of 250 urban, economically-disadvantaged, primarily minority women. In particular, we focus on differences between those saying that they have no chance whatsoever of contracting HIV and those who indicated at least some possibility of becoming HIV-infected. Three research questions are addressed: (1) Are there differences between these groups attributable to their risk behavior practices? (2) To what extent do women who think that they are not at risk for HIV engage in risky behaviors that could expose them to HIV? (3) What are the most salient predictors of the women's perceived risk classification? Results showed that women perceiving themselves to have at least some HIV risk engaged in higher rates of risky behaviors than their counterparts who perceived themselves to have no possibility of contracting HIV. Despite this finding, more than one-half of the "no perceived risk of HIV" sample had engaged in at least one risky practice during the preceding year and more than one-quarter had engaged in at least two such behaviors. Age, childhood maltreatment experiences, self-esteem, number of HIV risk behaviors practiced, amount of illegal drug use reported, and number of times having sex were significant predictors of women's perception of having some HIV risk versus having no HIV risk.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".