STD/HIV prevention intervention: efficacy, effectiveness and population impact
Bibliographic record
Abstract
The Global HIV Prevention Working Group recently stated: “We should be winning in HIV prevention. There are effective means to prevent every mode of transmission; political commitment on HIV has never been stronger; and financing for HIV programs in low-and-middle income countries increased six fold between 2001 and 2006 … If comprehensive HIV prevention were brought to scale, half of the infections projected to occur by 2015 could be averted.”1 Since the recognition of HIV as the cause of AIDS, considerable resources—both human and economic—have been allocated to the development and evaluation of efficacious interventions to halt the transmission of the virus and the progression of the disease. For an even longer period, researchers have worked to develop and evaluate efficacious interventions to prevent the transmission of other sexually transmitted pathogens and to treat and cure the diseases caused by them. The interventions studied have included behavioural interventions, treatments, vaccines, vaginal microbicides and male circumcision. A recent review of trials of interventions to prevent sexual transmission of all sexually transmitted infections (STI) including HIV identified 83 trials of individual, group or community-level interventions and concluded that, although many interventions have been found to be effective against STI including HIV, few have been replicated, widely implemented or carefully evaluated for effectiveness in other settings.2 In order to stop the spread of an infection in populations, the right intervention must be delivered to the right people at the right scale, the delivery of interventions must be sustained and the adherence of individuals must be ensured. There is a considerable gap between the development and evaluation of an efficacious intervention and the implementation of the correct mix of interventions at the right scale in populations in order to achieve population-level impact. Even in the case of individual-level biomedical interventions such as …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".