The rapidly changing paradigm of HIV prevention: time to strengthen social and behavioural approaches
Bibliographic record
Abstract
A decade after the world's leaders committed to fight the global HIV epidemic, UNAIDS notes progress in halting the spread of the virus. Access to treatment has in particular increased, with noticeable beneficial effects on HIV-related mortality. Further scaling-up treatment requires substantial human and financial resources and the continued investments that are required may further erode the limited resources for HIV prevention. Treatment can play a role in reducing the transmission of HIV, but treatment alone is not enough and cost-effective behavioural prevention approaches are available that in recent years have received less priority. HIV prevention may in the future benefit from novel biomedical approaches that are in development, including those that capitalize on the use of treatment. To date, evidence of effectiveness of biomedical prevention in real-life conditions is limited and, while they can increase prevention options, many biomedical prevention approaches will continue to rely on the behaviours of individuals and communities. These behaviors are shaped and constrained by the social, cultural, political and economic contexts that affect the vulnerability of individuals and communities. At the start of the 4(th) decade of the epidemic, it is timely to re-focus on strengthening the theory and practice of behavioural prevention of HIV.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.022 | 0.061 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".