Second generation HIV surveillance in Pakistan: evidence for understanding the epidemic and planning a response
Bibliographic record
Abstract
More than a decade ago UNAIDS and the WHO published a set of guidelines for the conduct of what was termed ‘second-generation HIV surveillance’, which encouraged the enhancement of HIV surveillance to provide better information about the patterns, status and trajectory of the HIV epidemic to better inform policies and programmes.1 As Rehle and colleagues have noted, key aspects of the second-generation approach include: combining information from different components of surveillance to achieve higher degrees of explanatory power, targeting segments of the population in which most new HIV infections are occurring; and integrating surveillance based on biological markers of infection (ie, HIV serosurveillance) with behavioural data.2 Since then, second-generation surveillance has become widely used in diverse global contexts, with substantial emphasis on two key components. The first component, sentinel HIV surveillance, has been conducted at convenient venues to track the amplitude and trends of HIV prevalence among populations based on presumed stratification of risk, such as women attending antenatal clinics, men and women attending public sexually transmitted disease clinics, and key populations at higher risk (such as female sex workers) receiving HIV prevention services. While this approach has provided important insights into the distribution and trends of HIV in different population segments, it has substantial constraints. Perhaps most importantly, it sacrifices representativeness for sampling efficiency, leading to uncertainty as to how well the results truly reflect the HIV prevalence in these populations. In addition, sentinel surveillance does not incorporate any assessment of behavioural trends, or the linkage between biological, socio-demographic, and behavioural variables with HIV status. As a result, it does not provide information about patterns and trends in the key behaviours that influence epidemics, nor does it permit a more in-depth understanding of relationships between socio-demographic characteristics and behaviours with HIV prevalence. To address this deficit, the …
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".