If HIV Prevention Works, Why Are Rates of High-Risk Sexual Behavior Increasing among MSM?
Bibliographic record
Abstract
Systematic reviews of HIV prevention research provide clear evidence that behavioral interventions can influence the sexual behavior of men who have sex with men (MSM). However, if HIV prevention works, why are rates of high-risk sexual behavior increasing among MSM in major European, Australian, Canadian, and U.S. cities? The evidence generated by systematic reviews alone may not provide a clear answer to this question. This is because (a) it is uncertain whether experimental interventions shown to be effective in one setting, place, or moment in time can be repeated successfully in another; (b) we have limited understanding of the processes that underlie the interventions; (c) interventions shown to work in an experimental study may not necessarily be effective in everyday life. To answer the question, we need to be alert to the changing risk environment in which men have sex with other men. We also need to develop a new program of research addressing the transferability, sustainability, and effectiveness of sexual health promotion among MSM. Randomized controlled trials will remain one of the optimal means of evaluating behavioral interventions in such a program. By further strengthening the evidence base, we may identify opportunities for innovative as well as effective HIV prevention initiatives.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".