Improving the Quality and Quantity of HIV Data in the Middle East and North Africa: Key Challenges and Ways Forward
Bibliographic record
Abstract
Although the HIV pandemic is witnessing a decline in the number of new infections in most regions of the world, the Middle East and North Africa (MENA) has a rapidly growing HIV problem. While generating HIV data has been consistently increasing since 2005, MENA's contribution to the global HIV literature is just over 1% and the existing evidence often falls behind the academic standards. Several factors could be at play that contribute to the limited quantity and quality of HIV data in MENA. This editorial tries to explore and explain the barriers to collecting high-quality HIV data and generating precise estimates in MENA. These barriers include a number of logistic and socio-political challenges faced by researchers, public health officials, and policy-makers. Looking at successful regional HIV programs, we explore examples were policies have shifted and lessons could be learned in developing appropriate responses to HIV across the region.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".