Self-perception of risk for HIV acquisition among people in prisons in Iran: A nationwide survey in 2017
Bibliographic record
Abstract
BACKGROUND: Self-perception of risk for HIV acquisition is crucial for promoting preventive behaviors among people in prisons. This cross-sectional study examined self-perception of HIV risk and associated factors among people in prisons in Iran. METHODS: In 2017, we conducted a cross-sectional study in Iran using multistage random sampling approach to recruit participants from 33 prisons in Iran. Eligible participants were adult people in prisons for at least one week. Data were collected via gender-matched face-to-face interviews using a standard HIV risk assessment questionnaire. Multivariable logistic regression models were built to identify correlates of high self-perceived HIV risk. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were reported. RESULTS: Among 5,422 HIV-negative participants (94.0% men; mean age 35.7 years), 6.8% (95% CI: 6.1-7.5%) reported high self-perception of risk for HIV acquisition. Among these, 3.6%, 5.2%, and 8.4% reported zero, one, and more than one risky behavior, respectively. Higher odds of high self-perceived risk were associated with being < 30 years old (AOR: 1.50; 95% CI: 1.13-2.00), having never got married (AOR: 1.58; 95% CI: 1.12-2.23), engaging in condomless sex (AOR: 1.56; 95% CI: 1.13-2.16), same-sex practices among men (AOR: 1.90; 95% CI: 1.43-2.51), and recent sexually transmitted infection symptoms (AOR: 2.18; 95% CI: 1.19-3.57). CONCLUSIONS: A notable gap exists between actual HIV risk behaviors and self-perceived risk among people in prisons in Iran. Targeted prison-based educational interventions are essential to improve risk awareness and promote HIV prevention behaviors among this marginalized 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".