P2-S6.09 Factors associated with HIV sexual risk management among HIV-negative men who have sex with men living in Quebec and who use internet to find sexual partners
Bibliographic record
Abstract
Background This presentation has 2 primary objectives. First, to describe sociodemographic, psychosocial, sociosexual, environnemental and health predictors of a HIV seroconversion risk behaviour, unprotected anal intercourse with HIV-positive or HIV-unknown casual partners (UAI(+/?)), among HIV-negative MSM who live in Quebec and use Internet to find sexual partners. Second, to analyse factors associated to those predictors. Method We use data from Net Gay Baromètre 2008, a online quantitative survey which took place on Quebec’ dating websites from December 2007 to May 2008. 3718 MSM participated in this survey of whom 1794 were HIV-negative. Bivariate analysis (χ2; t test) and hierarchical regression were performed with SPSS v0.16.0 for Macintosch. Results 14.7% of HIV-negative respondents have declared an UAI(+/?) in the past 12 months. Multivariate analysis show that homosexual identity, number of casual partners, marginal sexual practices, drug use, engagement in a couple relationship and history of a STI in the past 12 months were significant predictors associated with UAI(+/?) among those respondents. Bivariate analysis show that those predictors were more often declared among respondents who live in Montreal region, are seeking sensations, are seeking partners in sex venues and are regularly seeking partners on dating websites. Conclusions This group of predictors and associated factors shows various sexual scenarios. Those sexual scenarios seem to be more frequent in certain spaces like Internet, which influence the management of sexual risks. Initiatives to prevent HIV seroconversion adapted to the reality of MSM who live in Quebec and use Internet to find sexual partners are proposed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.056 | 0.002 |
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".