Regional variations in child and mother’s characteristics influencing the use of insecticide treated net in Nigeria
Bibliographic record
Abstract
Nigeria accounts for a quarter of malaria cases worldwide, which can be prevented with the use of insecticide treated nets (ITN). While studies have documented mother-related characteristics influencing use of ITN, regional variations in the influence of those factors are not well known. This study investigated nine factors (age, place of residence, education, religion, wealth, number of children in the household, sex of child, age of child and previous experience of child mortality) as possible predictors of use of ITN for children and how the associations vary across northern and southern parts of the country. The study utilised the 2015 Nigeria Malaria Indicator Survey, which comprised 6524 mothers (4009 from the north and 2151 from the south) aged 15-49. Bivariate and multivariate logistic regression models were fitted. It was found that, less than half (47.9%) of the respondents reported no access to a mosquito net in the north compared to 70.8% in the south. More than half (51.4%) of the northern respondents used insecticide treated net (ITN) for the child compared to 27.1% of southern mothers. When the variables are fitted together in the same model, place of residence, mother's age, mother's education, wealth, religion, number of children in the household and previous experience of child mortality were associated with the use of ITN. Regional variations exist in the influence of mother's age, number of children in the household and previous experience of child mortality. It was submitted that mother's characteristics are more important than the child's factors in the use of ITN, and that, contrary to the theory of poor utilisation of health-related facilities in the north compared to the south, residents in the former have access to and use ITN more than their counterparts from the latter.
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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 | 0.000 | 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".