Mapping and Situation Assessment of Key Populations at High Risk of HIV in Three Cities of Afghanistan
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
As yet, little is known about the HIV \n epidemic status and potential in Afghanistan. The country \n seems to be at an early epidemic phase with low HIV \n prevalence, but there are a number of underlying \n vulnerability factors that could lead to the conditions for \n epidemic expansion, including drug trafficking, the \n post-conflict situation with displacement of populations, a \n fledgling health care system, and a low level of knowledge \n and awareness about HIV/AIDS. As in other parts of central \n and south Asia, the most important proximate determinants of \n the scale and distribution of an HIV epidemic in Afghanistan \n will be the size and characteristics of high risk networks \n involving injecting drug users (IDUs), female sex workers \n (FSWs) and men who have sex with men (MSM) who are at high \n risk. Assessments from elsewhere in central Asia indicate an \n explosive growth in injecting drug use and commercial sex \n work throughout the region, concurrent epidemics of sexually \n transmitted infections (STIs), and economic and political \n migration. As yet, little information is known about the \n size, distribution, and characteristics of IDU and sex \n worker sub-populations in Afghanistan. Therefore, the World \n Bank (WB) agreed with the Ministry of Public Health (MOPH) \n to contract with the University of Manitoba (UM) to conduct \n an assessment of these three key, high risk populations in \n three cities of Afghanistan (Mazar-i-Sharif, Jalalabad, and Kabul).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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