Geographical/Ecological Differentials in Insecticide-Treated Net Use among Under-Five Children in Somolu Local Government Area, Lagos State
Bibliographic record
Abstract
Malaria control efforts currently lay emphasis on reducing transmission by limiting human-vector contact. More studies have been carried out on mosquito avoidance practices in the rural areas, leaving the urban areas understudied. This study was conducted to identify knowledge of malaria transmission and to investigate geographical/ecological differentials in the use of insecticide-treated nets (ITNs) among caregivers of under-fives in Somolu Local Government Area, Lagos State. A household survey was conducted by interviewing 394 female caregivers of under-fives selected using the WHO Lot Quality Technique from communities stratified based on level of planning and drainage. The mean age of the respondents was 33.6 ± 7.7 years. Malaria transmission was attributed mostly to mosquito bites in all strata: S1 (58.3%), S2 (56.1%) and S3 (61.4%). Mosquito net was mentioned as a preventive measure by: 59.3% (S1), 80.7% (S2) and 64.3% (S3). Ownership of long-lasting insecticidal nets was: 76.0% (S1), 75.4% (S2) and 68.6% (S3), and of these, 73.1% (S1), 70.7% (S2) and 72.4% (S3) reported that their child slept under the net the night before the survey. There is a need to reinforce education on transmission and ownership of ITNs especially among caregivers in unplanned, poorly drained 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".