Risks and benefits of internet use among people living with HIV/AIDS in Peru
Bibliographic record
Abstract
Objective: To evaluate use of the Internet for seeking sex partners and information on HIV and/or sexually transmitted infections (STIs) among people living with HIV/AIDS (PLWHA). Materials and methods: We interviewed consecutive PLWHA attending at Via Libre, in Lima, Peru, between May and June 2004. Results: Of 100 PLWHA, (46 men who had sex with men [MSM], 18 heterosexual men and 36 women), 59% reported using the Internet in the last 12 months. Of these, the majority (73%) accessed the Internet at a public place; 16 (27,1%) reported having gone online to search for sexual partners; and five (8,5%) reported having had sex with a partner found over the Internet. All sex seekers were men, of them a greater percentage were MSM rather than heterosexual (94% vs. 6%, p= 0,032). All five respondents who reported having had sex with a partner found online were MSM. Of those who accessed the Internet in the past year, 76% (47/59) had used it to find information on HIV/AIDS, and 39% (23/59) had used it to seek information on other STIs. Conclusions: Among PLWHA interviewed more than half reported Internet access. More than a quarter -almost exclusively MSMhad used it to search for sex partners, however, the majority had used the Internet to look for information about HIV/AIDS. These findings suggest that the Internet offers a convenient tool to engage high-risk MSM in online HIV/STI 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".