HIV Prevention 6 - Coming to Terms with Complexity : A Call to Action for HIV Prevention
Bibliographic record
Abstract
A quarter of a century of AIDS responses has created a huge body of knowledge about HIV transmission and how to prevent it, yet every day, around the world, nearly 7000 people become infected with the virus. Although HIV prevention is complex, it ought not to be mystifying. Local and national achievements in curbing the epidemic have been myriad, and have created a body of evidence about what works, but these successful approaches have not yet been fully applied. Essential programmes and services have not had sufficient coverage; they have often lacked the funding to be applied with sufficient quality and intensity. Action and funding have not necessarily been directed to where the epidemic is or to what drives it. Few programmes address vulnerability to HIV and structural determinants of the epidemic. A prevention constituency has not been adequately mobilised to stimulate the demand for HIV prevention. Confident and unified leadership has not emerged to assert what is needed in HIV prevention and how to overcome the political, sociocultural, and logistic barriers in getting there. We discuss the combination of solutions which are needed to intensify HIV prevention, using the existing body of evidence and the lessons from our successes and failures in HIV prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.033 | 0.041 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".