HIV-Positive Men who Use the Internet to Meet Other Men in France: Results from the French “Net Gay Baromètre” Study
Bibliographic record
Abstract
Objectives: With data from this biennial study, we will present some of the HIV-positive respondents’ social and sexual practices and identify predictors of regular unprotected anal intercourse in this group. Method: Men were recruited between January and April 2009 (n = 19 052) through personalized e-mailing on nine major websites for France’s diverse MSM population. Approximately 12 % of the sample was HIV-positive (n = 2229). Bivariate analyses (c2, t-test) as well as simple and multivariate logistic regressions were performed. Results: Compared to HIV-negative men, a significantly greater proportion of HIV-positive respondents were older, identified as homosexual, resided in the Paris metropolitan area, and were active members of the gay community. In particular, more HIV-positive men went to sex clubs, outdoor sex venues, and saunas in the previous 12 months. Drug use over this period of time was also more prevalent among HIV-positive men and this latter group had twice as many sexual partners as HIV-negative men. A significantly greater number of HIV-positive men reported regularly engaging in unprotected anal sex and about three times as many of them had “barebacked” in the past 12 months, compared to HIV-negative men: HIV-negative men engaged in less serosorting. The multivariate regression analysis found that regularly engaging in unprotected anal sex was significantly associated in HIV-positive men with the following factors: residing in the Paris metropolitan area, having more than 11 casual partners in the previous 12 months, meeting casual partners online, using dating websites for MSM that feature “hard” sexual practices, using drugs in the past 12 months, having contracted an STI over this period of time, not having been tested for HIV over this same period of time, engaging in bareback sex with a couple, having been in contact with a partner’s sperm during oral sex, engaging in group sex, and practicing fist fucking. Conclusion: This online survey reached many HIV-positive respondents. Overall, these men seem to be more exposed to some risk factors. This can be explained by positive HIV status but this could also be explained by sensation seeking, by a taste for risk, as well as by a disinterest in preventive strategies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".