HIV/AIDS in African militaries: an ecological analysis
Bibliographic record
Abstract
The HIV/AIDS pandemic is considered a security threat. Policy-makers have warned of destabilization of militaries due to massive troop deaths. Estimates of the rate of HIV within African militaries have been as high as 90 per cent. We aimed to determine if HIV prevalence within African militaries is higher than their host nation prevalence rates. Using systematic searching and access to United States Department of Defense data, we abstracted data on prevalence within militaries and their host communities. We conducted a random effects pooled analysis to determine differences in HIV prevalence rates in the military versus the host population. We obtained data on 21 African militaries. In general, HIV prevalence within the military is elevated compared to the general population. The differences were significant (odds ratio 1.97, 95% confidence interval: 1.58-2.45, P < 0.001). Further, inflated rates of HIV in militaries compared to non-military males of similar age were also significant (6.09, 4.47-8.30, P < or = 0.0001). States with recent conflicts and wars had elevated military rates, but these were also not significant (P = 0.4). Population levels predicted military prevalence rates (P < or = 0.001). HIV/AIDS prevalence rates in most African militaries are significantly elevated compared to their host communities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".