Socio-economic determinants of ownership and use of treated bed nets in Nigeria: results from a cross-sectional study in Cross River and Bauchi States in 2011
Bibliographic record
Abstract
BACKGROUND: Poor people bear a disproportionate burden of malaria and prevention measures may not reach them well. A study carried out to examine the socio-economic factors associated with ownership and use of treated bed nets in Cross River and Bauchi States of Nigeria took place soon after campaigns to distribute treated bed nets. METHODS: A cross-sectional household survey about childhood illnesses among mothers of children less than four years of age and focus group discussions in 90 communities in each of the two states asked about household ownership of treated bed nets and their use for children under four years old. Bivariate and multivariate analyses examined associations between socio-economic and other variables and these outcomes in each state. RESULTS: Some 72% of 7,685 households in Cross River and 87% of 5,535 households in Bauchi State had at least one treated bed net. In Cross River, urban households were more likely to possess bed nets, as were less-poor households (enough food in the last week), those with a male head, and those from communities with a formal health facility. In Bauchi, less-poor households and those with a more educated head were more likely to possess nets. In households with nets, only about half of children under four years old always slept under a net: 54% of 11,267 in Cross River and 57% of 11,277 in Bauchi. Factors associated with use of nets for young children in Cross River were less-poor households, fewer young children in the household, more education of the father, antenatal care of the mother, and younger age of the child, while in Bauchi the factors were a mother with more education and antenatal care, and younger age of the child. Some focus groups complained of distribution difficulties, and many described misconceptions about adverse effects of nets as an important reason for not using them. CONCLUSION: Despite a recent campaign to distribute treated bed nets, disadvantaged households were less likely to possess them and to use them for young children. Efforts are needed to reach these households and to dispel fears about dangers of using treated nets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".