Evidence from epidemic appraisals in Nigeria
Bibliographic record
Abstract
Although HIV prevalence has increased in most-at-risk populations (MARPs) across Nigeria, effective programming was difficult because Nigeria lacked information for prevention programmes to target interventions that maximise coverage and cost effectiveness. Epidemic appraisals (EA) were conducted in eight states to provide evidence for the planning, implementation and co-ordination of prevention interventions. Component 1: Mapping determined the size, typology and locations of MARPs. Component 2: Venue profiling identified and profiled venues where general populations engaged in high-risk behaviours. Component 3: Rural appraisals provided insights into risk behaviours and sexual networking in villages. States used mapping results to prioritise areas with a MARP coverage of 70% – 80% and then scale up interventions for non-brothel-based female sex workers (FSWs) instead of focusing on brothel-based FSWs. The eight states prioritisedf unding for the high-coverage areas to ensure a minimum coverage level of 70% – 80% of MARPs was reached. The refocused resources led to cost efficiencies. Applying venue profiling results, six states implemented interventions at bars and night clubs – previously not covered. States also maximised intervention coverage for high-risk general populations; this led to the use of resources for general population interventions in a focused way rather than across an entire state. States focused on condom programmes in rural areas. EA results provided the evidence for focusing interventions for high MARP coverage as well as forhigh-risk general populations. The states applied the results and rapidly refocused their interventions, increasing the likelihood of having an impact on HIV transmission in those states. Nigeria is now implementing EAs in the remaining 29 states to effect national-level impact.
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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 | 0.114 | 0.402 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".