Drivers of STD/HIV epidemiology and the timing and targets of STD/HIV prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the turn of the century insights into sexually transmitted disease (STD)/HIV epidemiology and prevention have proliferated. Accumulating empirical data and mathematical modelling efforts interactively point to a number of grounded generalisations that enhance our understanding of the spread of STIs including HIV in populations. These insights have important implications for the design and implementation of prevention programmes: they can guide expectations around the magnitude and shape of STI/HIV epidemics in the absence of prevention and control programmes; they can guide thoughts about when to implement prevention strategies, which subgroups to target and how to define required coverage; and they can help interpret programme successes and failures.1 An important generalisation is about the central role of population-level parameters in determining the magnitude and shape of STI epidemics. Whereas individual-level parameters may influence which individuals in a given population acquire infection, it is population-level parameters that affect the presence and prevalence of infection to be acquired. ### Sexual structure Sexual structure is a population-level parameter which increasingly emerges as an important determinant of whether major epidemics emerge in populations. The size and distribution of high-risk groups, or core groups, is an aspect of sexual structure that has received attention over the years.2–5 High-risk groups include sex workers, clients of sex workers, injecting drug users (IDUs) and men having sex with men. In specific areas other groups such as truck drivers or miners may also be defined as high-risk groups. A recent analysis suggests that the number of infected sex workers in a country, measured as a percentage of the total female adult population age 15–49 years, is highly positively correlated with country-wide HIV/AIDS prevalence levels.6 Although this analysis probably overstates the importance of sex work in determining the size of epidemics in southern Africa, in much of the world the …
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it