Evaluation of household coverage with long-lasting insecticidal nets in central Côte d’Ivoire
Bibliographic record
Abstract
BACKGROUND: To reduce malaria burden in Côte d'Ivoire, the Ministry of Health aims for 90% of its population to possess one long-lasting insecticidal net (LLIN) for every two persons by 2025. This study evaluated LLIN coverage two years after a mass distribution in central Côte d'Ivoire. METHODS: A census was conducted in 43 villages. Data were collected on household geo-position, composition, number of sleeping units and LLINs owned. LLIN coverage was assessed using: 1/ownership; proportion of household with at least one LLIN; 2/household access; households with sufficient nets for every two persons and for every sleeping unit; and 3/population access; proportion of population with access to LLIN within households and sleeping units. RESULTS: 10,630 households (89.6% response rate) and 46,619 inhabitants were recruited. Household LLIN ownership was 63.8% (95% CI: 58.7-68.8). Household LLIN access was 37.6% (95% CI: 33.2-42.0) based on 1 LLIN per 2 persons and 37.1% (95% CI: 33.0-41.2) based on 1 net per sleeping unit. Population LLIN access based on 1 LLIN per 2 persons and 1 net per sleeping space was 53.3% (95% CI: 48.6-58.1) and 49.4% (95% CI: 45.1-53.6), respectively. Approximately 17% of households with access for every 2 persons did not have access by every sleeping unit and 9.7% of households with access by sleeping unit did not have access for every 2 persons. Households with adequate access by sleeping unit but not for every 2 persons tend to be larger with fewer sleeping units, and have children under 5 years old and female members. The largest households (>7 members) and households with at least one under-five member had the lowest access (20.8 and 27.3%, respectively). CONCLUSION: LLIN access was low in this area of intense indoor malaria transmission, 2 years after the last mass distribution campaign. Strategies are needed to improve LLINs coverage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".