Malaria Knowledge and Bednet Use for Children Under Five: Angola Malaria Indicator Survey
Bibliographic record
Abstract
Despite distribution of millions of free mosquito nets in Angola malaria remains the primary cause of mortality in young children, accounting for 35% of deaths among children under five (CU5). Here, our objectives were to examine the association between malaria knowledge and bednet use for CU5, and the impact of malaria messaging. This study used responses from a nationally representative sample of women aged 15–49 from the Angola Malaria Indicator Survey (2011). Descriptive statistics, and multivariable logistic regression analyses were conducted. Among 6,576 residents with CU5 55.9% (n=3,697) did not own a bednet. Of the respondents with ≥1 bednet for sleeping 87.4% (n=2,122) identified mosquitos as a cause of malaria. Adjusting for respondents’ age, region, and education those reporting mosquitos as a cause of malaria had 1.7 (95%CI: 1.3–2.2) times the odds of bednet use for CU5 than those not reporting mosquitos as a malaria cause. Malaria messaging appeared to have little influence on CU5 bednet use. This study provides evidence of an association between malaria knowledge and bednet use, indicating that along with widescale distribution of bednets for malaria prevention, public health efforts in Angola should focus on increasing awareness and promoting bednet usage through targeted risk communication.
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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 | 0.002 |
| 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 | 0.001 |
| 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".