Social-Cognitive Predictors of Consistent Condom Use Among Young People in Moscow
Bibliographic record
Abstract
CONTEXT: Russia is experiencing an explosive HIV epidemic, and young people aged 15–29 have the highest incidence of infection. Understanding factors associated with condom use in this age-group is important in developing effective prevention interventions. METHODS: Telephone interviews were conducted with 1,203 Muscovites aged 15–29 in September 2002 to assess condom use, HIV knowledge and sexual behavior. Multivariate logistic regression was used to determine independent predictors of consistent condom use. RESULTS: Forty-four percent of sexually experienced respondents reported using condoms consistently. In multivariate analysis, the likelihood of consistent use was elevated among single women and men (odds ratios, 1.8 and 2.6, respectively), those who considered condoms reliable protection against unwanted pregnancy (2.4 and 1.6) and those who believed that most of their peers use condoms (2.9 and 4.6). For women, having recently had multiple partners was associated with increased odds of consistent use, and consistent use declined with age. CONCLUSION: Strategies to promote condom use should increase awareness about their effectiveness against not only unwanted pregnancies but also HIV and other STDs. Condoms should be recommended for married couples and people with one permanent partner as a contraceptive option as well as for disease prevention.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".